id	sid	tid	token	lemma	pos
cana-5102	1	1	communications	communication	NOUN
cana-5102	1	2	on	on	ADP
cana-5102	1	3	applied	apply	VERB
cana-5102	1	4	nonlinear	nonlinear	ADJ
cana-5102	1	5	analysis	analysis	NOUN
cana-5102	1	6	issn	issn	NOUN
cana-5102	1	7	:	:	PUNCT
cana-5102	1	8	1074	1074	NUM
cana-5102	1	9	-	-	PUNCT
cana-5102	1	10	133x	133x	NUM
cana-5102	1	11	vol	vol	NOUN
cana-5102	1	12	32	32	NUM
cana-5102	1	13	no	no	NOUN
cana-5102	1	14	.	.	PUNCT
cana-5102	2	1	icmasd	icmasd	NOUN
cana-5102	2	2	(	(	PUNCT
cana-5102	2	3	2025	2025	NUM
cana-5102	2	4	)	)	PUNCT
cana-5102	2	5	755	755	NUM
cana-5102	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	2	7	comparative	comparative	ADJ
cana-5102	2	8	study	study	NOUN
cana-5102	2	9	of	of	ADP
cana-5102	2	10	character	character	NOUN
cana-5102	2	11	recognition	recognition	NOUN
cana-5102	2	12	for	for	ADP
cana-5102	2	13	handwritten	handwritten	ADJ
cana-5102	2	14	characters	character	NOUN
cana-5102	2	15	tarun	tarun	PROPN
cana-5102	2	16	kumar1	kumar1	PROPN
cana-5102	2	17	,	,	PUNCT
cana-5102	2	18	arvin	arvin	PROPN
cana-5102	2	19	vinayek1	vinayek1	PROPN
cana-5102	2	20	,	,	PUNCT
cana-5102	2	21	pradip	pradip	PROPN
cana-5102	2	22	kumar	kumar	PROPN
cana-5102	2	23	yadava2	yadava2	PROPN
cana-5102	3	1	1school	1school	NUM
cana-5102	3	2	of	of	ADP
cana-5102	3	3	engineering	engineering	NOUN
cana-5102	3	4	and	and	CCONJ
cana-5102	3	5	technology	technology	NOUN
cana-5102	3	6	,	,	PUNCT
cana-5102	3	7	ct	ct	PROPN
cana-5102	3	8	university	university	PROPN
cana-5102	3	9	,	,	PUNCT
cana-5102	3	10	ludhiana	ludhiana	PROPN
cana-5102	3	11	,	,	PUNCT
cana-5102	3	12	punjab	punjab	PROPN
cana-5102	3	13	,	,	PUNCT
cana-5102	3	14	142024	142024	NUM
cana-5102	3	15	,	,	PUNCT
cana-5102	3	16	india	india	PROPN
cana-5102	3	17	2	2	NUM
cana-5102	3	18	department	department	NOUN
cana-5102	3	19	of	of	ADP
cana-5102	3	20	computer	computer	NOUN
cana-5102	3	21	science	science	NOUN
cana-5102	3	22	,	,	PUNCT
cana-5102	3	23	sunrise	sunrise	VERB
cana-5102	3	24	university	university	NOUN
cana-5102	3	25	alwar	alwar	NOUN
cana-5102	3	26	,	,	PUNCT
cana-5102	3	27	rajasthan	rajasthan	PROPN
cana-5102	3	28	,	,	PUNCT
cana-5102	3	29	301028	301028	NUM
cana-5102	3	30	,	,	PUNCT
cana-5102	3	31	india	india	PROPN
cana-5102	3	32	article	article	PROPN
cana-5102	3	33	history	history	NOUN
cana-5102	3	34	:	:	PUNCT
cana-5102	3	35	received	receive	VERB
cana-5102	3	36	:	:	PUNCT
cana-5102	3	37	12	12	NUM
cana-5102	3	38	-	-	SYM
cana-5102	3	39	12	12	NUM
cana-5102	3	40	-	-	PUNCT
cana-5102	3	41	2024	2024	NUM
cana-5102	3	42	revised	revise	VERB
cana-5102	3	43	:	:	PUNCT
cana-5102	3	44	25	25	NUM
cana-5102	3	45	-	-	PUNCT
cana-5102	3	46	01	01	NUM
cana-5102	3	47	-	-	PUNCT
cana-5102	3	48	2025	2025	NUM
cana-5102	3	49	accepted	accept	VERB
cana-5102	3	50	:	:	PUNCT
cana-5102	3	51	05	05	NUM
cana-5102	3	52	-	-	PUNCT
cana-5102	3	53	02	02	NUM
cana-5102	3	54	-	-	PUNCT
cana-5102	3	55	2025	2025	NUM
cana-5102	3	56	abstract	abstract	NOUN
cana-5102	3	57	:	:	PUNCT
cana-5102	3	58	optical	optical	ADJ
cana-5102	3	59	character	character	NOUN
cana-5102	3	60	recognition	recognition	NOUN
cana-5102	3	61	(	(	PUNCT
cana-5102	3	62	ocr	ocr	NOUN
cana-5102	3	63	)	)	PUNCT
cana-5102	3	64	represents	represent	VERB
cana-5102	3	65	an	an	DET
cana-5102	3	66	important	important	ADJ
cana-5102	3	67	technology	technology	NOUN
cana-5102	3	68	in	in	ADP
cana-5102	3	69	the	the	DET
cana-5102	3	70	context	context	NOUN
cana-5102	3	71	of	of	ADP
cana-5102	3	72	computer	computer	NOUN
cana-5102	3	73	vision	vision	NOUN
cana-5102	3	74	,	,	PUNCT
cana-5102	3	75	which	which	PRON
cana-5102	3	76	facilitates	facilitate	VERB
cana-5102	3	77	the	the	DET
cana-5102	3	78	extraction	extraction	NOUN
cana-5102	3	79	of	of	ADP
cana-5102	3	80	textual	textual	ADJ
cana-5102	3	81	data	datum	NOUN
cana-5102	3	82	with	with	ADP
cana-5102	3	83	the	the	DET
cana-5102	3	84	help	help	NOUN
cana-5102	3	85	images	image	NOUN
cana-5102	3	86	.	.	PUNCT
cana-5102	4	1	although	although	SCONJ
cana-5102	4	2	a	a	DET
cana-5102	4	3	lot	lot	NOUN
cana-5102	4	4	of	of	ADP
cana-5102	4	5	investigations	investigation	NOUN
cana-5102	4	6	have	have	AUX
cana-5102	4	7	examined	examine	VERB
cana-5102	4	8	various	various	ADJ
cana-5102	4	9	ocr	ocr	ADJ
cana-5102	4	10	models	model	NOUN
cana-5102	4	11	,	,	PUNCT
cana-5102	4	12	there	there	PRON
cana-5102	4	13	is	be	VERB
cana-5102	4	14	a	a	DET
cana-5102	4	15	notable	notable	ADJ
cana-5102	4	16	absence	absence	NOUN
cana-5102	4	17	of	of	ADP
cana-5102	4	18	comparative	comparative	ADJ
cana-5102	4	19	studies	study	NOUN
cana-5102	4	20	evaluating	evaluate	VERB
cana-5102	4	21	different	different	ADJ
cana-5102	4	22	algorithms	algorithm	NOUN
cana-5102	4	23	on	on	ADP
cana-5102	4	24	a	a	DET
cana-5102	4	25	unified	unified	ADJ
cana-5102	4	26	standardized	standardized	ADJ
cana-5102	4	27	dataset	dataset	NOUN
cana-5102	4	28	.	.	PUNCT
cana-5102	5	1	this	this	DET
cana-5102	5	2	research	research	NOUN
cana-5102	5	3	targets	target	NOUN
cana-5102	5	4	to	to	PART
cana-5102	5	5	fill	fill	VERB
cana-5102	5	6	this	this	DET
cana-5102	5	7	void	void	NOUN
cana-5102	5	8	by	by	ADP
cana-5102	5	9	assessing	assess	VERB
cana-5102	5	10	multiple	multiple	ADJ
cana-5102	5	11	ocr	ocr	ADJ
cana-5102	5	12	models	model	NOUN
cana-5102	5	13	across	across	ADP
cana-5102	5	14	two	two	NUM
cana-5102	5	15	distinct	distinct	ADJ
cana-5102	5	16	datasets	dataset	NOUN
cana-5102	5	17	:	:	PUNCT
cana-5102	5	18	one	one	NUM
cana-5102	5	19	consisting	consist	VERB
cana-5102	5	20	of	of	ADP
cana-5102	5	21	28×28	28×28	NOUN
cana-5102	5	22	-	-	ADJ
cana-5102	5	23	pixel	pixel	ADJ
cana-5102	5	24	images	image	NOUN
cana-5102	5	25	and	and	CCONJ
cana-5102	5	26	the	the	DET
cana-5102	5	27	other	other	ADJ
cana-5102	5	28	comprising	comprising	NOUN
cana-5102	5	29	of	of	ADP
cana-5102	5	30	64×64	64×64	PROPN
cana-5102	5	31	-	-	ADJ
cana-5102	5	32	pixel	pixel	ADJ
cana-5102	5	33	images	image	NOUN
cana-5102	5	34	.	.	PUNCT
cana-5102	6	1	the	the	DET
cana-5102	6	2	models	model	NOUN
cana-5102	6	3	evaluated	evaluate	VERB
cana-5102	6	4	include	include	VERB
cana-5102	6	5	ten	ten	NUM
cana-5102	6	6	convolutional	convolutional	ADJ
cana-5102	6	7	neural	neural	ADJ
cana-5102	6	8	networks	network	NOUN
cana-5102	6	9	(	(	PUNCT
cana-5102	6	10	cnns	cnns	PROPN
cana-5102	6	11	)	)	PUNCT
cana-5102	6	12	characterized	characterize	VERB
cana-5102	6	13	by	by	ADP
cana-5102	6	14	diverse	diverse	ADJ
cana-5102	6	15	activation	activation	NOUN
cana-5102	6	16	functions	function	NOUN
cana-5102	6	17	,	,	PUNCT
cana-5102	6	18	architectural	architectural	ADJ
cana-5102	6	19	depths	depth	NOUN
cana-5102	6	20	,	,	PUNCT
cana-5102	6	21	and	and	CCONJ
cana-5102	6	22	,	,	PUNCT
cana-5102	6	23	dropout	dropout	NOUN
cana-5102	6	24	rates	rate	NOUN
cana-5102	6	25	,	,	PUNCT
cana-5102	6	26	in	in	ADP
cana-5102	6	27	addition	addition	NOUN
cana-5102	6	28	to	to	ADP
cana-5102	6	29	long	long	ADJ
cana-5102	6	30	short	short	ADJ
cana-5102	6	31	-	-	PUNCT
cana-5102	6	32	term	term	NOUN
cana-5102	6	33	memory	memory	NOUN
cana-5102	6	34	(	(	PUNCT
cana-5102	6	35	lstm	lstm	NOUN
cana-5102	6	36	)	)	PUNCT
cana-5102	6	37	networks	network	NOUN
cana-5102	6	38	,	,	PUNCT
cana-5102	6	39	support	support	VERB
cana-5102	6	40	vector	vector	NOUN
cana-5102	6	41	machines	machine	NOUN
cana-5102	6	42	(	(	PUNCT
cana-5102	6	43	svm	svm	PROPN
cana-5102	6	44	)	)	PUNCT
cana-5102	6	45	,	,	PUNCT
cana-5102	6	46	encoder	encoder	NOUN
cana-5102	6	47	-	-	PUNCT
cana-5102	6	48	decoder	decoder	NOUN
cana-5102	6	49	frameworks	framework	NOUN
cana-5102	6	50	,	,	PUNCT
cana-5102	6	51	and	and	CCONJ
cana-5102	6	52	random	random	ADJ
cana-5102	6	53	forest	forest	NOUN
cana-5102	6	54	classifiers	classifier	NOUN
cana-5102	6	55	.	.	PUNCT
cana-5102	7	1	through	through	ADP
cana-5102	7	2	our	our	PRON
cana-5102	7	3	analysis	analysis	NOUN
cana-5102	7	4	,	,	PUNCT
cana-5102	7	5	it	it	PRON
cana-5102	7	6	was	be	AUX
cana-5102	7	7	revealed	reveal	VERB
cana-5102	7	8	that	that	SCONJ
cana-5102	7	9	the	the	DET
cana-5102	7	10	cnn	cnn	PROPN
cana-5102	7	11	-	-	PUNCT
cana-5102	7	12	based	base	VERB
cana-5102	7	13	models	model	NOUN
cana-5102	7	14	demonstrate	demonstrate	VERB
cana-5102	7	15	exceptional	exceptional	ADJ
cana-5102	7	16	performance	performance	NOUN
cana-5102	7	17	,	,	PUNCT
cana-5102	7	18	with	with	ADP
cana-5102	7	19	the	the	DET
cana-5102	7	20	leading	lead	VERB
cana-5102	7	21	64×64	64×64	NUM
cana-5102	7	22	cnn	cnn	PROPN
cana-5102	7	23	model	model	NOUN
cana-5102	7	24	achieving	achieve	VERB
cana-5102	7	25	an	an	DET
cana-5102	7	26	accuracy	accuracy	NOUN
cana-5102	7	27	of	of	ADP
cana-5102	7	28	0.9882	0.9882	NUM
cana-5102	7	29	,	,	PUNCT
cana-5102	7	30	while	while	SCONJ
cana-5102	7	31	the	the	DET
cana-5102	7	32	highest	highest	ADV
cana-5102	7	33	-	-	PUNCT
cana-5102	7	34	performing	perform	VERB
cana-5102	7	35	28×28	28×28	NUM
cana-5102	7	36	cnn	cnn	NOUN
cana-5102	7	37	model	model	NOUN
cana-5102	7	38	reported	report	VERB
cana-5102	7	39	an	an	DET
cana-5102	7	40	accuracy	accuracy	NOUN
cana-5102	7	41	of	of	ADP
cana-5102	7	42	0.9763	0.9763	NUM
cana-5102	7	43	.	.	PUNCT
cana-5102	8	1	the	the	DET
cana-5102	8	2	encoder	encoder	NOUN
cana-5102	8	3	-	-	PUNCT
cana-5102	8	4	decoder	decoder	NOUN
cana-5102	8	5	model	model	NOUN
cana-5102	8	6	also	also	ADV
cana-5102	8	7	showed	show	VERB
cana-5102	8	8	formidable	formidable	ADJ
cana-5102	8	9	results	result	NOUN
cana-5102	8	10	,	,	PUNCT
cana-5102	8	11	achieving	achieve	VERB
cana-5102	8	12	an	an	DET
cana-5102	8	13	accuracy	accuracy	NOUN
cana-5102	8	14	of	of	ADP
cana-5102	8	15	0.9781	0.9781	NUM
cana-5102	8	16	on	on	ADP
cana-5102	8	17	the	the	DET
cana-5102	8	18	64×64	64×64	NUM
cana-5102	8	19	dataset	dataset	NOUN
cana-5102	8	20	and	and	CCONJ
cana-5102	8	21	0.9810	0.9810	NUM
cana-5102	8	22	on	on	ADP
cana-5102	8	23	the	the	DET
cana-5102	8	24	28×28	28×28	NUM
cana-5102	8	25	dataset	dataset	NOUN
cana-5102	8	26	.	.	PUNCT
cana-5102	9	1	svm	svm	PROPN
cana-5102	9	2	exhibited	exhibit	VERB
cana-5102	9	3	robust	robust	ADJ
cana-5102	9	4	performance	performance	NOUN
cana-5102	9	5	on	on	ADP
cana-5102	9	6	the	the	DET
cana-5102	9	7	higher	high	ADJ
cana-5102	9	8	-	-	PUNCT
cana-5102	9	9	resolution	resolution	NOUN
cana-5102	9	10	dataset	dataset	NOUN
cana-5102	9	11	,	,	PUNCT
cana-5102	9	12	achieving	achieve	VERB
cana-5102	9	13	an	an	DET
cana-5102	9	14	accuracy	accuracy	NOUN
cana-5102	9	15	of	of	ADP
cana-5102	9	16	0.9777	0.9777	NUM
cana-5102	9	17	,	,	PUNCT
cana-5102	9	18	but	but	CCONJ
cana-5102	9	19	encountered	encounter	VERB
cana-5102	9	20	significant	significant	ADJ
cana-5102	9	21	challenges	challenge	NOUN
cana-5102	9	22	on	on	ADP
cana-5102	9	23	the	the	DET
cana-5102	9	24	28×28	28×28	NUM
cana-5102	9	25	dataset	dataset	NOUN
cana-5102	9	26	,	,	PUNCT
cana-5102	9	27	where	where	SCONJ
cana-5102	9	28	the	the	DET
cana-5102	9	29	accuracy	accuracy	NOUN
cana-5102	9	30	of	of	ADP
cana-5102	9	31	only	only	ADJ
cana-5102	9	32	0.7252	0.7252	NUM
cana-5102	9	33	was	be	AUX
cana-5102	9	34	reported	report	VERB
cana-5102	9	35	.	.	PUNCT
cana-5102	10	1	the	the	DET
cana-5102	10	2	random	random	ADJ
cana-5102	10	3	forest	forest	NOUN
cana-5102	10	4	classifier	classifier	NOUN
cana-5102	10	5	maintained	maintain	VERB
cana-5102	10	6	a	a	DET
cana-5102	10	7	consistent	consistent	ADJ
cana-5102	10	8	accuracy	accuracy	NOUN
cana-5102	10	9	of	of	ADP
cana-5102	10	10	0.9538	0.9538	NUM
cana-5102	10	11	across	across	ADP
cana-5102	10	12	both	both	DET
cana-5102	10	13	datasets	dataset	NOUN
cana-5102	10	14	.	.	PUNCT
cana-5102	11	1	conversely	conversely	ADV
cana-5102	11	2	,	,	PUNCT
cana-5102	11	3	lstm	lstm	NOUN
cana-5102	11	4	models	model	NOUN
cana-5102	11	5	struggled	struggle	VERB
cana-5102	11	6	to	to	PART
cana-5102	11	7	generalize	generalize	VERB
cana-5102	11	8	effectively	effectively	ADV
cana-5102	11	9	for	for	ADP
cana-5102	11	10	ocr	ocr	PROPN
cana-5102	11	11	applications	application	NOUN
cana-5102	11	12	,	,	PUNCT
cana-5102	11	13	with	with	ADP
cana-5102	11	14	the	the	DET
cana-5102	11	15	best	good	ADJ
cana-5102	11	16	lstm	lstm	ADJ
cana-5102	11	17	model	model	NOUN
cana-5102	11	18	achieving	achieve	VERB
cana-5102	11	19	a	a	DET
cana-5102	11	20	mere	mere	ADJ
cana-5102	11	21	0.0346	0.0346	NUM
cana-5102	11	22	accuracy	accuracy	NOUN
cana-5102	11	23	on	on	ADP
cana-5102	11	24	the	the	DET
cana-5102	11	25	64×64	64×64	NUM
cana-5102	11	26	dataset	dataset	NOUN
cana-5102	11	27	and	and	CCONJ
cana-5102	11	28	performing	perform	VERB
cana-5102	11	29	poorly	poorly	ADV
cana-5102	11	30	on	on	ADP
cana-5102	11	31	the	the	DET
cana-5102	11	32	28×28	28×28	NUM
cana-5102	11	33	dataset	dataset	NOUN
cana-5102	11	34	as	as	ADV
cana-5102	11	35	well	well	ADV
cana-5102	11	36	.	.	PUNCT
cana-5102	12	1	through	through	ADP
cana-5102	12	2	the	the	DET
cana-5102	12	3	comprehensive	comprehensive	ADJ
cana-5102	12	4	study	study	NOUN
cana-5102	12	5	of	of	ADP
cana-5102	12	6	cnn	cnn	PROPN
cana-5102	12	7	model	model	PROPN
cana-5102	12	8	,	,	PUNCT
cana-5102	12	9	it	it	PRON
cana-5102	12	10	was	be	AUX
cana-5102	12	11	observed	observe	VERB
cana-5102	12	12	that	that	SCONJ
cana-5102	12	13	,	,	PUNCT
cana-5102	12	14	model	model	NOUN
cana-5102	12	15	3	3	NUM
cana-5102	12	16	attained	attain	VERB
cana-5102	12	17	the	the	DET
cana-5102	12	18	highest	high	ADJ
cana-5102	12	19	accuracy	accuracy	NOUN
cana-5102	12	20	of	of	ADP
cana-5102	12	21	0.9907	0.9907	NUM
cana-5102	12	22	across	across	ADP
cana-5102	12	23	both	both	DET
cana-5102	12	24	datasets	dataset	NOUN
cana-5102	12	25	,	,	PUNCT
cana-5102	12	26	accompanied	accompany	VERB
cana-5102	12	27	by	by	ADP
cana-5102	12	28	minimal	minimal	ADJ
cana-5102	12	29	validation	validation	NOUN
cana-5102	12	30	loss	loss	NOUN
cana-5102	12	31	(	(	PUNCT
cana-5102	12	32	0.0377	0.0377	NUM
cana-5102	12	33	for	for	ADP
cana-5102	12	34	28×28	28×28	NUM
cana-5102	12	35	and	and	CCONJ
cana-5102	12	36	0.0577	0.0577	NUM
cana-5102	12	37	for	for	ADP
cana-5102	12	38	64×64	64×64	NUM
cana-5102	12	39	)	)	PUNCT
cana-5102	12	40	.	.	PUNCT
cana-5102	13	1	other	other	ADJ
cana-5102	13	2	cnn	cnn	PROPN
cana-5102	13	3	models	model	NOUN
cana-5102	13	4	exhibited	exhibit	VERB
cana-5102	13	5	varying	vary	VERB
cana-5102	13	6	levels	level	NOUN
cana-5102	13	7	of	of	ADP
cana-5102	13	8	performance	performance	NOUN
cana-5102	13	9	,	,	PUNCT
cana-5102	13	10	with	with	ADP
cana-5102	13	11	deeper	deep	ADJ
cana-5102	13	12	architectures	architecture	NOUN
cana-5102	13	13	generally	generally	ADV
cana-5102	13	14	surpassing	surpass	VERB
cana-5102	13	15	their	their	PRON
cana-5102	13	16	shallower	shallow	ADJ
cana-5102	13	17	counterparts	counterpart	NOUN
cana-5102	13	18	.	.	PUNCT
cana-5102	14	1	our	our	PRON
cana-5102	14	2	methodology	methodology	NOUN
cana-5102	14	3	encompassed	encompass	VERB
cana-5102	14	4	preprocessing	preprocesse	VERB
cana-5102	14	5	the	the	DET
cana-5102	14	6	datasets	dataset	NOUN
cana-5102	14	7	,	,	PUNCT
cana-5102	14	8	partitioning	partition	VERB
cana-5102	14	9	them	they	PRON
cana-5102	14	10	into	into	ADP
cana-5102	14	11	training	training	NOUN
cana-5102	14	12	and	and	CCONJ
cana-5102	14	13	testing	testing	NOUN
cana-5102	14	14	sets	set	NOUN
cana-5102	14	15	,	,	PUNCT
cana-5102	14	16	and	and	CCONJ
cana-5102	14	17	training	train	VERB
cana-5102	14	18	each	each	DET
cana-5102	14	19	model	model	NOUN
cana-5102	14	20	with	with	ADP
cana-5102	14	21	suitable	suitable	ADJ
cana-5102	14	22	hyperparameters	hyperparameter	NOUN
cana-5102	14	23	.	.	PUNCT
cana-5102	15	1	the	the	DET
cana-5102	15	2	findings	finding	NOUN
cana-5102	15	3	underscore	underscore	VERB
cana-5102	15	4	that	that	SCONJ
cana-5102	15	5	cnn	cnn	PROPN
cana-5102	15	6	-	-	PUNCT
cana-5102	15	7	based	base	VERB
cana-5102	15	8	architectures	architecture	NOUN
cana-5102	15	9	are	be	AUX
cana-5102	15	10	the	the	DET
cana-5102	15	11	most	most	ADV
cana-5102	15	12	effective	effective	ADJ
cana-5102	15	13	for	for	ADP
cana-5102	15	14	ocr	ocr	ADJ
cana-5102	15	15	tasks	task	NOUN
cana-5102	15	16	,	,	PUNCT
cana-5102	15	17	particularly	particularly	ADV
cana-5102	15	18	at	at	ADP
cana-5102	15	19	elevated	elevated	ADJ
cana-5102	15	20	resolutions	resolution	NOUN
cana-5102	15	21	.	.	PUNCT
cana-5102	16	1	these	these	DET
cana-5102	16	2	results	result	NOUN
cana-5102	16	3	offer	offer	VERB
cana-5102	16	4	significant	significant	ADJ
cana-5102	16	5	insights	insight	NOUN
cana-5102	16	6	into	into	ADP
cana-5102	16	7	the	the	DET
cana-5102	16	8	efficacy	efficacy	NOUN
cana-5102	16	9	of	of	ADP
cana-5102	16	10	various	various	ADJ
cana-5102	16	11	ocr	ocr	ADJ
cana-5102	16	12	models	model	NOUN
cana-5102	16	13	.	.	PUNCT
cana-5102	17	1	keywords	keyword	NOUN
cana-5102	17	2	:	:	PUNCT
cana-5102	17	3	recognition	recognition	NOUN
cana-5102	17	4	,	,	PUNCT
cana-5102	17	5	comprehensive	comprehensive	ADJ
cana-5102	17	6	,	,	PUNCT
cana-5102	17	7	encompassed	encompass	VERB
cana-5102	17	8	,	,	PUNCT
cana-5102	17	9	hyperparameters	hyperparameter	NOUN
cana-5102	17	10	.	.	PUNCT
cana-5102	18	1	communications	communication	NOUN
cana-5102	18	2	on	on	ADP
cana-5102	18	3	applied	apply	VERB
cana-5102	18	4	nonlinear	nonlinear	ADJ
cana-5102	18	5	analysis	analysis	NOUN
cana-5102	18	6	issn	issn	NOUN
cana-5102	18	7	:	:	PUNCT
cana-5102	18	8	1074	1074	NUM
cana-5102	18	9	-	-	PUNCT
cana-5102	18	10	133x	133x	NUM
cana-5102	18	11	vol	vol	NOUN
cana-5102	18	12	32	32	NUM
cana-5102	18	13	no	no	NOUN
cana-5102	18	14	.	.	PUNCT
cana-5102	19	1	icmasd	icmasd	NOUN
cana-5102	19	2	(	(	PUNCT
cana-5102	19	3	2025	2025	NUM
cana-5102	19	4	)	)	PUNCT
cana-5102	19	5	756	756	NUM
cana-5102	20	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	20	2	introduction	introduction	NOUN
cana-5102	20	3	optical	optical	ADJ
cana-5102	20	4	character	character	NOUN
cana-5102	20	5	recognition	recognition	NOUN
cana-5102	20	6	(	(	PUNCT
cana-5102	20	7	ocr	ocr	NOUN
cana-5102	20	8	)	)	PUNCT
cana-5102	20	9	powered	power	VERB
cana-5102	20	10	by	by	ADP
cana-5102	20	11	machine	machine	NOUN
cana-5102	20	12	learning	learning	NOUN
cana-5102	20	13	has	have	AUX
cana-5102	20	14	become	become	VERB
cana-5102	20	15	an	an	DET
cana-5102	20	16	important	important	ADJ
cana-5102	20	17	tool	tool	NOUN
cana-5102	20	18	across	across	ADP
cana-5102	20	19	various	various	ADJ
cana-5102	20	20	industries	industry	NOUN
cana-5102	20	21	and	and	CCONJ
cana-5102	20	22	applications	application	NOUN
cana-5102	20	23	.	.	PUNCT
cana-5102	21	1	the	the	DET
cana-5102	21	2	ability	ability	NOUN
cana-5102	21	3	to	to	PART
cana-5102	21	4	convert	convert	VERB
cana-5102	21	5	printed	print	VERB
cana-5102	21	6	or	or	CCONJ
cana-5102	21	7	handwritten	handwritten	ADJ
cana-5102	21	8	text	text	NOUN
cana-5102	21	9	into	into	ADP
cana-5102	21	10	a	a	DET
cana-5102	21	11	digital	digital	ADJ
cana-5102	21	12	format	format	NOUN
cana-5102	21	13	has	have	AUX
cana-5102	21	14	made	make	VERB
cana-5102	21	15	it	it	PRON
cana-5102	21	16	vital	vital	ADJ
cana-5102	21	17	for	for	ADP
cana-5102	21	18	tasks	task	NOUN
cana-5102	21	19	such	such	ADJ
cana-5102	21	20	as	as	ADP
cana-5102	21	21	document	document	NOUN
cana-5102	21	22	digitization	digitization	NOUN
cana-5102	21	23	,	,	PUNCT
cana-5102	21	24	automated	automate	VERB
cana-5102	21	25	verification	verification	NOUN
cana-5102	21	26	,	,	PUNCT
cana-5102	21	27	and	and	CCONJ
cana-5102	21	28	real	real	ADJ
cana-5102	21	29	-	-	PUNCT
cana-5102	21	30	time	time	NOUN
cana-5102	21	31	translation	translation	NOUN
cana-5102	21	32	.	.	PUNCT
cana-5102	22	1	by	by	ADP
cana-5102	22	2	utilizing	utilize	VERB
cana-5102	22	3	machine	machine	NOUN
cana-5102	22	4	learning	learning	NOUN
cana-5102	22	5	algorithms	algorithm	NOUN
cana-5102	22	6	,	,	PUNCT
cana-5102	22	7	ocr	ocr	ADJ
cana-5102	22	8	systems	system	NOUN
cana-5102	22	9	improve	improve	VERB
cana-5102	22	10	their	their	PRON
cana-5102	22	11	text	text	NOUN
cana-5102	22	12	recognition	recognition	NOUN
cana-5102	22	13	and	and	CCONJ
cana-5102	22	14	processing	processing	NOUN
cana-5102	22	15	capabilities	capability	NOUN
cana-5102	22	16	,	,	PUNCT
cana-5102	22	17	enhancing	enhance	VERB
cana-5102	22	18	efficiency	efficiency	NOUN
cana-5102	22	19	and	and	CCONJ
cana-5102	22	20	reducing	reduce	VERB
cana-5102	22	21	the	the	DET
cana-5102	22	22	manual	manual	ADJ
cana-5102	22	23	effort	effort	NOUN
cana-5102	22	24	needed	need	VERB
cana-5102	22	25	to	to	PART
cana-5102	22	26	handle	handle	VERB
cana-5102	22	27	large	large	ADJ
cana-5102	22	28	volumes	volume	NOUN
cana-5102	22	29	of	of	ADP
cana-5102	22	30	textual	textual	ADJ
cana-5102	22	31	data	datum	NOUN
cana-5102	22	32	.	.	PUNCT
cana-5102	23	1	this	this	DET
cana-5102	23	2	technology	technology	NOUN
cana-5102	23	3	is	be	AUX
cana-5102	23	4	widely	widely	ADV
cana-5102	23	5	used	use	VERB
cana-5102	23	6	in	in	ADP
cana-5102	23	7	areas	area	NOUN
cana-5102	23	8	like	like	ADP
cana-5102	23	9	document	document	NOUN
cana-5102	23	10	scanning	scanning	NOUN
cana-5102	23	11	,	,	PUNCT
cana-5102	23	12	license	license	NOUN
cana-5102	23	13	plate	plate	NOUN
cana-5102	23	14	recognition	recognition	NOUN
cana-5102	23	15	,	,	PUNCT
cana-5102	23	16	automated	automate	VERB
cana-5102	23	17	data	data	NOUN
cana-5102	23	18	entry	entry	NOUN
cana-5102	23	19	,	,	PUNCT
cana-5102	23	20	and	and	CCONJ
cana-5102	23	21	accessibility	accessibility	NOUN
cana-5102	23	22	solutions	solution	NOUN
cana-5102	23	23	for	for	ADP
cana-5102	23	24	individuals	individual	NOUN
cana-5102	23	25	with	with	ADP
cana-5102	23	26	visual	visual	ADJ
cana-5102	23	27	impairments	impairment	NOUN
cana-5102	23	28	.	.	PUNCT
cana-5102	24	1	ocr	ocr	PROPN
cana-5102	24	2	can	can	AUX
cana-5102	24	3	primarily	primarily	ADV
cana-5102	24	4	be	be	AUX
cana-5102	24	5	categorized	categorize	VERB
cana-5102	24	6	into	into	ADP
cana-5102	24	7	two	two	NUM
cana-5102	24	8	types	type	NOUN
cana-5102	24	9	:	:	PUNCT
cana-5102	24	10	offline	offline	ADJ
cana-5102	24	11	and	and	CCONJ
cana-5102	24	12	online	online	ADJ
cana-5102	24	13	recognition	recognition	NOUN
cana-5102	24	14	.	.	PUNCT
cana-5102	25	1	offline	offline	PROPN
cana-5102	25	2	ocr	ocr	PROPN
cana-5102	25	3	is	be	AUX
cana-5102	25	4	used	use	VERB
cana-5102	25	5	to	to	PART
cana-5102	25	6	identify	identify	VERB
cana-5102	25	7	printed	print	VERB
cana-5102	25	8	or	or	CCONJ
cana-5102	25	9	handwritten	handwritten	ADJ
cana-5102	25	10	text	text	NOUN
cana-5102	25	11	from	from	ADP
cana-5102	25	12	scanned	scan	VERB
cana-5102	25	13	documents	document	NOUN
cana-5102	25	14	,	,	PUNCT
cana-5102	25	15	books	book	NOUN
cana-5102	25	16	,	,	PUNCT
cana-5102	25	17	or	or	CCONJ
cana-5102	25	18	images	image	NOUN
cana-5102	25	19	,	,	PUNCT
cana-5102	25	20	analyzing	analyze	VERB
cana-5102	25	21	the	the	DET
cana-5102	25	22	input	input	NOUN
cana-5102	25	23	after	after	SCONJ
cana-5102	25	24	it	it	PRON
cana-5102	25	25	has	have	AUX
cana-5102	25	26	been	be	AUX
cana-5102	25	27	captured	capture	VERB
cana-5102	25	28	.	.	PUNCT
cana-5102	26	1	on	on	ADP
cana-5102	26	2	the	the	DET
cana-5102	26	3	other	other	ADJ
cana-5102	26	4	hand	hand	NOUN
cana-5102	26	5	,	,	PUNCT
cana-5102	26	6	online	online	PROPN
cana-5102	26	7	ocr	ocr	PROPN
cana-5102	26	8	focuses	focus	VERB
cana-5102	26	9	on	on	ADP
cana-5102	26	10	real	real	ADJ
cana-5102	26	11	-	-	PUNCT
cana-5102	26	12	time	time	NOUN
cana-5102	26	13	text	text	NOUN
cana-5102	26	14	recognition	recognition	NOUN
cana-5102	26	15	,	,	PUNCT
cana-5102	26	16	often	often	ADV
cana-5102	26	17	involving	involve	VERB
cana-5102	26	18	digital	digital	ADJ
cana-5102	26	19	writing	writing	NOUN
cana-5102	26	20	inputs	input	NOUN
cana-5102	26	21	,	,	PUNCT
cana-5102	26	22	such	such	ADJ
cana-5102	26	23	as	as	ADP
cana-5102	26	24	handwriting	handwriting	NOUN
cana-5102	26	25	with	with	ADP
cana-5102	26	26	a	a	DET
cana-5102	26	27	stylus	stylus	NOUN
cana-5102	26	28	on	on	ADP
cana-5102	26	29	touch	touch	NOUN
cana-5102	26	30	-	-	PUNCT
cana-5102	26	31	sensitive	sensitive	ADJ
cana-5102	26	32	screens	screen	NOUN
cana-5102	26	33	.	.	PUNCT
cana-5102	27	1	both	both	DET
cana-5102	27	2	types	type	NOUN
cana-5102	27	3	rely	rely	VERB
cana-5102	27	4	on	on	ADP
cana-5102	27	5	advanced	advanced	ADJ
cana-5102	27	6	preprocessing	preprocessing	NOUN
cana-5102	27	7	techniques	technique	NOUN
cana-5102	27	8	to	to	PART
cana-5102	27	9	enhance	enhance	VERB
cana-5102	27	10	text	text	NOUN
cana-5102	27	11	recognition	recognition	NOUN
cana-5102	27	12	accuracy	accuracy	NOUN
cana-5102	27	13	by	by	ADP
cana-5102	27	14	reducing	reduce	VERB
cana-5102	27	15	noise	noise	NOUN
cana-5102	27	16	,	,	PUNCT
cana-5102	27	17	normalizing	normalizing	ADJ
cana-5102	27	18	inputs	input	NOUN
cana-5102	27	19	,	,	PUNCT
cana-5102	27	20	and	and	CCONJ
cana-5102	27	21	segmenting	segment	VERB
cana-5102	27	22	characters	character	NOUN
cana-5102	27	23	.	.	PUNCT
cana-5102	28	1	the	the	DET
cana-5102	28	2	effectiveness	effectiveness	NOUN
cana-5102	28	3	of	of	ADP
cana-5102	28	4	ocr	ocr	PROPN
cana-5102	28	5	is	be	AUX
cana-5102	28	6	affected	affect	VERB
cana-5102	28	7	by	by	ADP
cana-5102	28	8	several	several	ADJ
cana-5102	28	9	factors	factor	NOUN
cana-5102	28	10	,	,	PUNCT
cana-5102	28	11	which	which	PRON
cana-5102	28	12	include	include	VERB
cana-5102	28	13	,	,	PUNCT
cana-5102	28	14	the	the	DET
cana-5102	28	15	quality	quality	NOUN
cana-5102	28	16	of	of	ADP
cana-5102	28	17	input	input	NOUN
cana-5102	28	18	images	image	NOUN
cana-5102	28	19	,	,	PUNCT
cana-5102	28	20	feature	feature	NOUN
cana-5102	28	21	extraction	extraction	NOUN
cana-5102	28	22	methods	method	NOUN
cana-5102	28	23	,	,	PUNCT
cana-5102	28	24	classification	classification	NOUN
cana-5102	28	25	models	model	NOUN
cana-5102	28	26	,	,	PUNCT
cana-5102	28	27	and	and	CCONJ
cana-5102	28	28	the	the	DET
cana-5102	28	29	specific	specific	ADJ
cana-5102	28	30	language	language	NOUN
cana-5102	28	31	or	or	CCONJ
cana-5102	28	32	script	script	NOUN
cana-5102	28	33	being	be	AUX
cana-5102	28	34	processed	process	VERB
cana-5102	28	35	.	.	PUNCT
cana-5102	29	1	regardless	regardless	ADV
cana-5102	29	2	of	of	ADP
cana-5102	29	3	significant	significant	ADJ
cana-5102	29	4	improvements	improvement	NOUN
cana-5102	29	5	in	in	ADP
cana-5102	29	6	ocr	ocr	PROPN
cana-5102	29	7	technology	technology	NOUN
cana-5102	29	8	,	,	PUNCT
cana-5102	29	9	there	there	PRON
cana-5102	29	10	are	be	VERB
cana-5102	29	11	some	some	DET
cana-5102	29	12	challenges	challenge	NOUN
cana-5102	29	13	.	.	PUNCT
cana-5102	30	1	variations	variation	NOUN
cana-5102	30	2	in	in	ADP
cana-5102	30	3	handwriting	handwriting	NOUN
cana-5102	30	4	styles	style	NOUN
cana-5102	30	5	,	,	PUNCT
cana-5102	30	6	low	low	ADJ
cana-5102	30	7	-	-	PUNCT
cana-5102	30	8	quality	quality	NOUN
cana-5102	30	9	scanned	scan	VERB
cana-5102	30	10	documents	document	NOUN
cana-5102	30	11	,	,	PUNCT
cana-5102	30	12	distorted	distort	VERB
cana-5102	30	13	or	or	CCONJ
cana-5102	30	14	overlapping	overlap	VERB
cana-5102	30	15	text	text	NOUN
cana-5102	30	16	,	,	PUNCT
cana-5102	30	17	and	and	CCONJ
cana-5102	30	18	complex	complex	ADJ
cana-5102	30	19	character	character	NOUN
cana-5102	30	20	structures	structure	NOUN
cana-5102	30	21	pose	pose	VERB
cana-5102	30	22	significant	significant	ADJ
cana-5102	30	23	hurdles	hurdle	NOUN
cana-5102	30	24	to	to	PART
cana-5102	30	25	achieve	achieve	VERB
cana-5102	30	26	high	high	ADJ
cana-5102	30	27	accuracy	accuracy	NOUN
cana-5102	30	28	.	.	PUNCT
cana-5102	31	1	while	while	SCONJ
cana-5102	31	2	deep	deep	ADJ
cana-5102	31	3	learning	learning	NOUN
cana-5102	31	4	-	-	PUNCT
cana-5102	31	5	based	base	VERB
cana-5102	31	6	ocr	ocr	ADJ
cana-5102	31	7	models	model	NOUN
cana-5102	31	8	have	have	AUX
cana-5102	31	9	shown	show	VERB
cana-5102	31	10	ample	ample	ADJ
cana-5102	31	11	improvements	improvement	NOUN
cana-5102	31	12	,	,	PUNCT
cana-5102	31	13	traditional	traditional	ADJ
cana-5102	31	14	methods	method	NOUN
cana-5102	31	15	still	still	ADV
cana-5102	31	16	hold	hold	VERB
cana-5102	31	17	relevance	relevance	NOUN
cana-5102	31	18	in	in	ADP
cana-5102	31	19	situations	situation	NOUN
cana-5102	31	20	where	where	SCONJ
cana-5102	31	21	computational	computational	ADJ
cana-5102	31	22	efficiency	efficiency	NOUN
cana-5102	31	23	is	be	AUX
cana-5102	31	24	critical	critical	ADJ
cana-5102	31	25	.	.	PUNCT
cana-5102	32	1	the	the	DET
cana-5102	32	2	growing	grow	VERB
cana-5102	32	3	demand	demand	NOUN
cana-5102	32	4	for	for	ADP
cana-5102	32	5	multilingual	multilingual	ADJ
cana-5102	32	6	ocr	ocr	PROPN
cana-5102	32	7	systems	system	NOUN
cana-5102	32	8	further	far	ADV
cana-5102	32	9	complicates	complicate	VERB
cana-5102	32	10	the	the	DET
cana-5102	32	11	landscape	landscape	NOUN
cana-5102	32	12	.	.	PUNCT
cana-5102	33	1	the	the	DET
cana-5102	33	2	main	main	ADJ
cana-5102	33	3	objectives	objective	NOUN
cana-5102	33	4	of	of	ADP
cana-5102	33	5	this	this	DET
cana-5102	33	6	research	research	NOUN
cana-5102	33	7	are	be	AUX
cana-5102	33	8	:	:	PUNCT
cana-5102	33	9	•	•	ADP
cana-5102	33	10	to	to	PART
cana-5102	33	11	summarize	summarize	VERB
cana-5102	33	12	existing	exist	VERB
cana-5102	33	13	ocr	ocr	ADJ
cana-5102	33	14	research	research	NOUN
cana-5102	33	15	based	base	VERB
cana-5102	33	16	on	on	ADP
cana-5102	33	17	different	different	ADJ
cana-5102	33	18	languages	language	NOUN
cana-5102	33	19	and	and	CCONJ
cana-5102	33	20	machine	machine	NOUN
cana-5102	33	21	learning	learn	VERB
cana-5102	33	22	techniques	technique	NOUN
cana-5102	33	23	.	.	PUNCT
cana-5102	34	1	•	•	ADP
cana-5102	34	2	to	to	PART
cana-5102	34	3	highlight	highlight	VERB
cana-5102	34	4	the	the	DET
cana-5102	34	5	strengths	strength	NOUN
cana-5102	34	6	and	and	CCONJ
cana-5102	34	7	weaknesses	weakness	NOUN
cana-5102	34	8	of	of	ADP
cana-5102	34	9	various	various	ADJ
cana-5102	34	10	ocr	ocr	ADJ
cana-5102	34	11	models	model	NOUN
cana-5102	34	12	.	.	PUNCT
cana-5102	35	1	•	•	NUM
cana-5102	35	2	to	to	PART
cana-5102	35	3	identify	identify	VERB
cana-5102	35	4	the	the	DET
cana-5102	35	5	most	most	ADV
cana-5102	35	6	efficient	efficient	ADJ
cana-5102	35	7	machine	machine	NOUN
cana-5102	35	8	learning	learning	NOUN
cana-5102	35	9	-	-	PUNCT
cana-5102	35	10	based	base	VERB
cana-5102	35	11	ocr	ocr	ADJ
cana-5102	35	12	model	model	NOUN
cana-5102	35	13	for	for	ADP
cana-5102	35	14	standardized	standardized	ADJ
cana-5102	35	15	datasets	dataset	NOUN
cana-5102	35	16	.	.	PUNCT
cana-5102	36	1	•	•	NUM
cana-5102	36	2	to	to	PART
cana-5102	36	3	provide	provide	VERB
cana-5102	36	4	a	a	DET
cana-5102	36	5	comparative	comparative	ADJ
cana-5102	36	6	analysis	analysis	NOUN
cana-5102	36	7	of	of	ADP
cana-5102	36	8	different	different	ADJ
cana-5102	36	9	ocr	ocr	ADJ
cana-5102	36	10	techniques	technique	NOUN
cana-5102	36	11	based	base	VERB
cana-5102	36	12	on	on	ADP
cana-5102	36	13	training	training	NOUN
cana-5102	36	14	and	and	CCONJ
cana-5102	36	15	testing	testing	NOUN
cana-5102	36	16	accuracy	accuracy	NOUN
cana-5102	36	17	.	.	PUNCT
cana-5102	37	1	research	research	NOUN
cana-5102	37	2	in	in	ADP
cana-5102	37	3	the	the	DET
cana-5102	37	4	field	field	NOUN
cana-5102	37	5	of	of	ADP
cana-5102	37	6	optical	optical	ADJ
cana-5102	37	7	character	character	NOUN
cana-5102	37	8	recognition	recognition	NOUN
cana-5102	37	9	(	(	PUNCT
cana-5102	37	10	ocr	ocr	NOUN
cana-5102	37	11	)	)	PUNCT
cana-5102	37	12	has	have	AUX
cana-5102	37	13	investigated	investigate	VERB
cana-5102	37	14	a	a	DET
cana-5102	37	15	diverse	diverse	ADJ
cana-5102	37	16	array	array	NOUN
cana-5102	37	17	of	of	ADP
cana-5102	37	18	methodologies	methodology	NOUN
cana-5102	37	19	,	,	PUNCT
cana-5102	37	20	encompassing	encompass	VERB
cana-5102	37	21	both	both	DET
cana-5102	37	22	conventional	conventional	ADJ
cana-5102	37	23	machine	machine	NOUN
cana-5102	37	24	learning	learn	VERB
cana-5102	37	25	techniques	technique	NOUN
cana-5102	37	26	,	,	PUNCT
cana-5102	37	27	such	such	ADJ
cana-5102	37	28	as	as	ADP
cana-5102	37	29	support	support	NOUN
cana-5102	37	30	vector	vector	NOUN
cana-5102	37	31	machines	machine	NOUN
cana-5102	37	32	and	and	CCONJ
cana-5102	37	33	hidden	hidden	ADJ
cana-5102	37	34	markov	markov	NOUN
cana-5102	37	35	models	model	NOUN
cana-5102	37	36	,	,	PUNCT
cana-5102	37	37	as	as	ADV
cana-5102	37	38	well	well	ADV
cana-5102	37	39	as	as	ADP
cana-5102	37	40	more	more	ADV
cana-5102	37	41	sophisticated	sophisticated	ADJ
cana-5102	37	42	deep	deep	ADJ
cana-5102	37	43	learning	learning	NOUN
cana-5102	37	44	strategies	strategy	NOUN
cana-5102	37	45	,	,	PUNCT
cana-5102	37	46	including	include	VERB
cana-5102	37	47	convolutional	convolutional	ADJ
cana-5102	37	48	neural	neural	ADJ
cana-5102	37	49	networks	network	NOUN
cana-5102	37	50	and	and	CCONJ
cana-5102	37	51	long	long	ADJ
cana-5102	37	52	short	short	ADJ
cana-5102	37	53	-	-	PUNCT
cana-5102	37	54	term	term	NOUN
cana-5102	37	55	memory	memory	NOUN
cana-5102	37	56	networks	network	NOUN
cana-5102	37	57	.	.	PUNCT
cana-5102	38	1	additionally	additionally	ADV
cana-5102	38	2	,	,	PUNCT
cana-5102	38	3	hybrid	hybrid	ADJ
cana-5102	38	4	models	model	NOUN
cana-5102	38	5	that	that	PRON
cana-5102	38	6	integrate	integrate	VERB
cana-5102	38	7	multiple	multiple	ADJ
cana-5102	38	8	methodologies	methodology	NOUN
cana-5102	38	9	have	have	AUX
cana-5102	38	10	been	be	AUX
cana-5102	38	11	developed	develop	VERB
cana-5102	38	12	to	to	PART
cana-5102	38	13	improve	improve	VERB
cana-5102	38	14	performance	performance	NOUN
cana-5102	38	15	,	,	PUNCT
cana-5102	38	16	especially	especially	ADV
cana-5102	38	17	in	in	ADP
cana-5102	38	18	the	the	DET
cana-5102	38	19	context	context	NOUN
cana-5102	38	20	of	of	ADP
cana-5102	38	21	recognizing	recognize	VERB
cana-5102	38	22	handwritten	handwritten	ADJ
cana-5102	38	23	or	or	CCONJ
cana-5102	38	24	degraded	degraded	ADJ
cana-5102	38	25	text	text	NOUN
cana-5102	38	26	.	.	PUNCT
cana-5102	39	1	despite	despite	SCONJ
cana-5102	39	2	numerous	numerous	ADJ
cana-5102	39	3	studies	study	NOUN
cana-5102	39	4	reporting	report	VERB
cana-5102	39	5	high	high	ADJ
cana-5102	39	6	accuracy	accuracy	NOUN
cana-5102	39	7	for	for	ADP
cana-5102	39	8	specific	specific	ADJ
cana-5102	39	9	models	model	NOUN
cana-5102	39	10	,	,	PUNCT
cana-5102	39	11	there	there	PRON
cana-5102	39	12	is	be	VERB
cana-5102	39	13	a	a	DET
cana-5102	39	14	scarcity	scarcity	NOUN
cana-5102	39	15	of	of	ADP
cana-5102	39	16	direct	direct	ADJ
cana-5102	39	17	comparisons	comparison	NOUN
cana-5102	39	18	among	among	ADP
cana-5102	39	19	different	different	ADJ
cana-5102	39	20	techniques	technique	NOUN
cana-5102	39	21	,	,	PUNCT
cana-5102	39	22	mainly	mainly	ADV
cana-5102	39	23	because	because	SCONJ
cana-5102	39	24	of	of	ADP
cana-5102	39	25	discrepancies	discrepancy	NOUN
cana-5102	39	26	in	in	ADP
cana-5102	39	27	datasets	dataset	NOUN
cana-5102	39	28	and	and	CCONJ
cana-5102	39	29	evaluation	evaluation	NOUN
cana-5102	39	30	criteria	criterion	NOUN
cana-5102	39	31	.	.	PUNCT
cana-5102	40	1	communications	communication	NOUN
cana-5102	40	2	on	on	ADP
cana-5102	40	3	applied	apply	VERB
cana-5102	40	4	nonlinear	nonlinear	ADJ
cana-5102	40	5	analysis	analysis	NOUN
cana-5102	40	6	issn	issn	NOUN
cana-5102	40	7	:	:	PUNCT
cana-5102	40	8	1074	1074	NUM
cana-5102	40	9	-	-	PUNCT
cana-5102	40	10	133x	133x	NUM
cana-5102	40	11	vol	vol	NOUN
cana-5102	40	12	32	32	NUM
cana-5102	40	13	no	no	NOUN
cana-5102	40	14	.	.	PUNCT
cana-5102	41	1	icmasd	icmasd	NOUN
cana-5102	41	2	(	(	PUNCT
cana-5102	41	3	2025	2025	NUM
cana-5102	41	4	)	)	PUNCT
cana-5102	41	5	757	757	PROPN
cana-5102	41	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	41	7	the	the	DET
cana-5102	41	8	methodology	methodology	NOUN
cana-5102	41	9	employed	employ	VERB
cana-5102	41	10	in	in	ADP
cana-5102	41	11	this	this	DET
cana-5102	41	12	research	research	NOUN
cana-5102	41	13	adopts	adopt	VERB
cana-5102	41	14	a	a	DET
cana-5102	41	15	structured	structured	ADJ
cana-5102	41	16	framework	framework	NOUN
cana-5102	41	17	for	for	ADP
cana-5102	41	18	evaluating	evaluate	VERB
cana-5102	41	19	various	various	ADJ
cana-5102	41	20	ocr	ocr	ADJ
cana-5102	41	21	models	model	NOUN
cana-5102	41	22	.	.	PUNCT
cana-5102	42	1	this	this	DET
cana-5102	42	2	process	process	NOUN
cana-5102	42	3	includes	include	VERB
cana-5102	42	4	data	datum	NOUN
cana-5102	42	5	preprocessing	preprocessing	NOUN
cana-5102	42	6	,	,	PUNCT
cana-5102	42	7	the	the	DET
cana-5102	42	8	selection	selection	NOUN
cana-5102	42	9	of	of	ADP
cana-5102	42	10	different	different	ADJ
cana-5102	42	11	machine	machine	NOUN
cana-5102	42	12	learning	learning	NOUN
cana-5102	42	13	and	and	CCONJ
cana-5102	42	14	deep	deep	ADJ
cana-5102	42	15	learning	learning	NOUN
cana-5102	42	16	models	model	NOUN
cana-5102	42	17	,	,	PUNCT
cana-5102	42	18	training	train	VERB
cana-5102	42	19	these	these	DET
cana-5102	42	20	models	model	NOUN
cana-5102	42	21	on	on	ADP
cana-5102	42	22	varying	vary	VERB
cana-5102	42	23	image	image	NOUN
cana-5102	42	24	resolutions	resolution	NOUN
cana-5102	42	25	,	,	PUNCT
cana-5102	42	26	and	and	CCONJ
cana-5102	42	27	evaluating	evaluate	VERB
cana-5102	42	28	their	their	PRON
cana-5102	42	29	performance	performance	NOUN
cana-5102	42	30	.	.	PUNCT
cana-5102	43	1	the	the	DET
cana-5102	43	2	effectiveness	effectiveness	NOUN
cana-5102	43	3	of	of	ADP
cana-5102	43	4	each	each	DET
cana-5102	43	5	model	model	NOUN
cana-5102	43	6	is	be	AUX
cana-5102	43	7	quantified	quantify	VERB
cana-5102	43	8	through	through	ADP
cana-5102	43	9	metrics	metric	NOUN
cana-5102	43	10	such	such	ADJ
cana-5102	43	11	as	as	ADP
cana-5102	43	12	accuracy	accuracy	NOUN
cana-5102	43	13	,	,	PUNCT
cana-5102	43	14	precision	precision	NOUN
cana-5102	43	15	,	,	PUNCT
cana-5102	43	16	and	and	CCONJ
cana-5102	43	17	computational	computational	ADJ
cana-5102	43	18	efficiency	efficiency	NOUN
cana-5102	43	19	,	,	PUNCT
cana-5102	43	20	with	with	ADP
cana-5102	43	21	the	the	DET
cana-5102	43	22	goal	goal	NOUN
cana-5102	43	23	of	of	ADP
cana-5102	43	24	identifying	identify	VERB
cana-5102	43	25	the	the	DET
cana-5102	43	26	most	most	ADV
cana-5102	43	27	effective	effective	ADJ
cana-5102	43	28	approach	approach	NOUN
cana-5102	43	29	.	.	PUNCT
cana-5102	44	1	by	by	ADP
cana-5102	44	2	conducting	conduct	VERB
cana-5102	44	3	comparisons	comparison	NOUN
cana-5102	44	4	of	of	ADP
cana-5102	44	5	different	different	ADJ
cana-5102	44	6	models	model	NOUN
cana-5102	44	7	under	under	ADP
cana-5102	44	8	uniform	uniform	ADJ
cana-5102	44	9	conditions	condition	NOUN
cana-5102	44	10	,	,	PUNCT
cana-5102	44	11	this	this	DET
cana-5102	44	12	research	research	NOUN
cana-5102	44	13	seeks	seek	VERB
cana-5102	44	14	to	to	PART
cana-5102	44	15	elucidate	elucidate	VERB
cana-5102	44	16	their	their	PRON
cana-5102	44	17	respective	respective	ADJ
cana-5102	44	18	strengths	strength	NOUN
cana-5102	44	19	and	and	CCONJ
cana-5102	44	20	weaknesses	weakness	NOUN
cana-5102	44	21	.	.	PUNCT
cana-5102	45	1	the	the	DET
cana-5102	45	2	outcomes	outcome	NOUN
cana-5102	45	3	of	of	ADP
cana-5102	45	4	this	this	DET
cana-5102	45	5	study	study	NOUN
cana-5102	45	6	reveal	reveal	VERB
cana-5102	45	7	that	that	SCONJ
cana-5102	45	8	ocr	ocr	NOUN
cana-5102	45	9	models	model	NOUN
cana-5102	45	10	based	base	VERB
cana-5102	45	11	on	on	ADP
cana-5102	45	12	deep	deep	ADJ
cana-5102	45	13	learning	learning	NOUN
cana-5102	45	14	,	,	PUNCT
cana-5102	45	15	particularly	particularly	ADV
cana-5102	45	16	those	those	PRON
cana-5102	45	17	utilizing	utilize	VERB
cana-5102	45	18	cnn	cnn	PROPN
cana-5102	45	19	architectures	architecture	NOUN
cana-5102	45	20	,	,	PUNCT
cana-5102	45	21	surpass	surpass	ADJ
cana-5102	45	22	traditional	traditional	ADJ
cana-5102	45	23	methods	method	NOUN
cana-5102	45	24	in	in	ADP
cana-5102	45	25	the	the	DET
cana-5102	45	26	recognition	recognition	NOUN
cana-5102	45	27	of	of	ADP
cana-5102	45	28	both	both	DET
cana-5102	45	29	printed	print	VERB
cana-5102	45	30	and	and	CCONJ
cana-5102	45	31	handwritten	handwritten	ADJ
cana-5102	45	32	text	text	NOUN
cana-5102	45	33	.	.	PUNCT
cana-5102	46	1	enhanced	enhance	VERB
cana-5102	46	2	image	image	NOUN
cana-5102	46	3	resolution	resolution	NOUN
cana-5102	46	4	correlates	correlate	VERB
cana-5102	46	5	with	with	ADP
cana-5102	46	6	improved	improve	VERB
cana-5102	46	7	recognition	recognition	NOUN
cana-5102	46	8	accuracy	accuracy	NOUN
cana-5102	46	9	,	,	PUNCT
cana-5102	46	10	although	although	SCONJ
cana-5102	46	11	certain	certain	ADJ
cana-5102	46	12	models	model	NOUN
cana-5102	46	13	exhibit	exhibit	VERB
cana-5102	46	14	difficulties	difficulty	NOUN
cana-5102	46	15	when	when	SCONJ
cana-5102	46	16	faced	face	VERB
cana-5102	46	17	with	with	ADP
cana-5102	46	18	variations	variation	NOUN
cana-5102	46	19	in	in	ADP
cana-5102	46	20	handwriting	handwriting	NOUN
cana-5102	46	21	and	and	CCONJ
cana-5102	46	22	distorted	distorted	ADJ
cana-5102	46	23	inputs	input	NOUN
cana-5102	46	24	.	.	PUNCT
cana-5102	47	1	these	these	DET
cana-5102	47	2	findings	finding	NOUN
cana-5102	47	3	underscore	underscore	VERB
cana-5102	47	4	the	the	DET
cana-5102	47	5	critical	critical	ADJ
cana-5102	47	6	role	role	NOUN
cana-5102	47	7	of	of	ADP
cana-5102	47	8	selecting	select	VERB
cana-5102	47	9	suitable	suitable	ADJ
cana-5102	47	10	preprocessing	preprocessing	NOUN
cana-5102	47	11	techniques	technique	NOUN
cana-5102	47	12	and	and	CCONJ
cana-5102	47	13	model	model	NOUN
cana-5102	47	14	architectures	architecture	NOUN
cana-5102	47	15	to	to	PART
cana-5102	47	16	achieve	achieve	VERB
cana-5102	47	17	optimal	optimal	ADJ
cana-5102	47	18	performance	performance	NOUN
cana-5102	47	19	in	in	ADP
cana-5102	47	20	ocr	ocr	ADJ
cana-5102	47	21	tasks	task	NOUN
cana-5102	47	22	.	.	PUNCT
cana-5102	48	1	although	although	SCONJ
cana-5102	48	2	considerable	considerable	ADJ
cana-5102	48	3	advancements	advancement	NOUN
cana-5102	48	4	have	have	AUX
cana-5102	48	5	been	be	AUX
cana-5102	48	6	made	make	VERB
cana-5102	48	7	in	in	ADP
cana-5102	48	8	optical	optical	ADJ
cana-5102	48	9	character	character	NOUN
cana-5102	48	10	recognition	recognition	NOUN
cana-5102	48	11	(	(	PUNCT
cana-5102	48	12	ocr	ocr	ADJ
cana-5102	48	13	)	)	PUNCT
cana-5102	48	14	technology	technology	NOUN
cana-5102	48	15	,	,	PUNCT
cana-5102	48	16	several	several	ADJ
cana-5102	48	17	challenges	challenge	NOUN
cana-5102	48	18	persist	persist	VERB
cana-5102	48	19	,	,	PUNCT
cana-5102	48	20	including	include	VERB
cana-5102	48	21	the	the	DET
cana-5102	48	22	need	need	NOUN
cana-5102	48	23	to	to	PART
cana-5102	48	24	enhance	enhance	VERB
cana-5102	48	25	accuracy	accuracy	NOUN
cana-5102	48	26	for	for	ADP
cana-5102	48	27	low	low	ADJ
cana-5102	48	28	-	-	PUNCT
cana-5102	48	29	quality	quality	NOUN
cana-5102	48	30	inputs	input	NOUN
cana-5102	48	31	,	,	PUNCT
cana-5102	48	32	accommodate	accommodate	VERB
cana-5102	48	33	a	a	DET
cana-5102	48	34	variety	variety	NOUN
cana-5102	48	35	of	of	ADP
cana-5102	48	36	handwriting	handwriting	NOUN
cana-5102	48	37	styles	style	NOUN
cana-5102	48	38	,	,	PUNCT
cana-5102	48	39	and	and	CCONJ
cana-5102	48	40	improve	improve	VERB
cana-5102	48	41	computational	computational	ADJ
cana-5102	48	42	efficiency	efficiency	NOUN
cana-5102	48	43	.	.	PUNCT
cana-5102	49	1	future	future	ADJ
cana-5102	49	2	research	research	NOUN
cana-5102	49	3	is	be	AUX
cana-5102	49	4	anticipated	anticipate	VERB
cana-5102	49	5	to	to	PART
cana-5102	49	6	concentrate	concentrate	VERB
cana-5102	49	7	on	on	ADP
cana-5102	49	8	the	the	DET
cana-5102	49	9	integration	integration	NOUN
cana-5102	49	10	of	of	ADP
cana-5102	49	11	ocr	ocr	NOUN
cana-5102	49	12	with	with	ADP
cana-5102	49	13	natural	natural	ADJ
cana-5102	49	14	language	language	NOUN
cana-5102	49	15	processing	processing	NOUN
cana-5102	49	16	to	to	PART
cana-5102	49	17	achieve	achieve	VERB
cana-5102	49	18	superior	superior	ADJ
cana-5102	49	19	contextual	contextual	ADJ
cana-5102	49	20	comprehension	comprehension	NOUN
cana-5102	49	21	,	,	PUNCT
cana-5102	49	22	the	the	DET
cana-5102	49	23	creation	creation	NOUN
cana-5102	49	24	of	of	ADP
cana-5102	49	25	lightweight	lightweight	ADJ
cana-5102	49	26	models	model	NOUN
cana-5102	49	27	suitable	suitable	ADJ
cana-5102	49	28	for	for	ADP
cana-5102	49	29	real	real	ADJ
cana-5102	49	30	-	-	PUNCT
cana-5102	49	31	time	time	NOUN
cana-5102	49	32	applications	application	NOUN
cana-5102	49	33	,	,	PUNCT
cana-5102	49	34	and	and	CCONJ
cana-5102	49	35	the	the	DET
cana-5102	49	36	enhancement	enhancement	NOUN
cana-5102	49	37	of	of	ADP
cana-5102	49	38	multilingual	multilingual	ADJ
cana-5102	49	39	ocr	ocr	ADJ
cana-5102	49	40	functionalities	functionality	NOUN
cana-5102	49	41	.	.	PUNCT
cana-5102	50	1	given	give	VERB
cana-5102	50	2	the	the	DET
cana-5102	50	3	increasing	increase	VERB
cana-5102	50	4	demand	demand	NOUN
cana-5102	50	5	for	for	ADP
cana-5102	50	6	automation	automation	NOUN
cana-5102	50	7	and	and	CCONJ
cana-5102	50	8	digital	digital	ADJ
cana-5102	50	9	text	text	NOUN
cana-5102	50	10	processing	processing	NOUN
cana-5102	50	11	,	,	PUNCT
cana-5102	50	12	ocr	ocr	PROPN
cana-5102	50	13	continues	continue	VERB
cana-5102	50	14	to	to	PART
cana-5102	50	15	be	be	AUX
cana-5102	50	16	an	an	DET
cana-5102	50	17	essential	essential	ADJ
cana-5102	50	18	technology	technology	NOUN
cana-5102	50	19	with	with	ADP
cana-5102	50	20	significant	significant	ADJ
cana-5102	50	21	implications	implication	NOUN
cana-5102	50	22	across	across	ADP
cana-5102	50	23	multiple	multiple	ADJ
cana-5102	50	24	industries	industry	NOUN
cana-5102	50	25	and	and	CCONJ
cana-5102	50	26	everyday	everyday	ADJ
cana-5102	50	27	uses	use	NOUN
cana-5102	50	28	.	.	PUNCT
cana-5102	51	1	literature	literature	NOUN
cana-5102	51	2	review	review	VERB
cana-5102	51	3	this	this	DET
cana-5102	51	4	paper	paper	NOUN
cana-5102	51	5	aims	aim	VERB
cana-5102	51	6	to	to	PART
cana-5102	51	7	examine	examine	VERB
cana-5102	51	8	current	current	ADJ
cana-5102	51	9	models	model	NOUN
cana-5102	51	10	and	and	CCONJ
cana-5102	51	11	machine	machine	NOUN
cana-5102	51	12	learning	learning	NOUN
cana-5102	51	13	methods	method	NOUN
cana-5102	51	14	used	use	VERB
cana-5102	51	15	for	for	ADP
cana-5102	51	16	optical	optical	ADJ
cana-5102	51	17	character	character	NOUN
cana-5102	51	18	recognition	recognition	NOUN
cana-5102	51	19	.	.	PUNCT
cana-5102	52	1	different	different	ADJ
cana-5102	52	2	languages	language	NOUN
cana-5102	52	3	use	use	VERB
cana-5102	52	4	different	different	ADJ
cana-5102	52	5	feature	feature	NOUN
cana-5102	52	6	extraction	extraction	NOUN
cana-5102	52	7	methods	method	NOUN
cana-5102	52	8	and	and	CCONJ
cana-5102	52	9	classify	classify	VERB
cana-5102	52	10	with	with	ADP
cana-5102	52	11	different	different	ADJ
cana-5102	52	12	classification	classification	NOUN
cana-5102	52	13	methods	method	NOUN
cana-5102	52	14	.	.	PUNCT
cana-5102	53	1	raus	raus	PROPN
cana-5102	53	2	et	et	PROPN
cana-5102	53	3	al	al	PROPN
cana-5102	53	4	.	.	PUNCT
cana-5102	54	1	[	[	X
cana-5102	54	2	1	1	X
cana-5102	54	3	]	]	PUNCT
cana-5102	54	4	used	use	VERB
cana-5102	54	5	ann	ann	PROPN
cana-5102	54	6	to	to	PART
cana-5102	54	7	recognize	recognize	VERB
cana-5102	54	8	the	the	DET
cana-5102	54	9	numeral	numeral	ADJ
cana-5102	54	10	character	character	NOUN
cana-5102	54	11	on	on	ADP
cana-5102	54	12	the	the	DET
cana-5102	54	13	licence	licence	NOUN
cana-5102	54	14	plate	plate	NOUN
cana-5102	54	15	.	.	PUNCT
cana-5102	55	1	the	the	DET
cana-5102	55	2	images	image	NOUN
cana-5102	55	3	are	be	AUX
cana-5102	55	4	compared	compare	VERB
cana-5102	55	5	pixel	pixel	PROPN
cana-5102	55	6	by	by	ADP
cana-5102	55	7	pixel	pixel	PROPN
cana-5102	55	8	to	to	PART
cana-5102	55	9	get	get	VERB
cana-5102	55	10	the	the	DET
cana-5102	55	11	information	information	NOUN
cana-5102	55	12	of	of	ADP
cana-5102	55	13	difference	difference	NOUN
cana-5102	55	14	between	between	ADP
cana-5102	55	15	two	two	NUM
cana-5102	55	16	images	image	NOUN
cana-5102	55	17	and	and	CCONJ
cana-5102	55	18	characters	character	NOUN
cana-5102	55	19	.	.	PUNCT
cana-5102	56	1	the	the	DET
cana-5102	56	2	recognition	recognition	NOUN
cana-5102	56	3	rate	rate	NOUN
cana-5102	56	4	comes	come	VERB
cana-5102	56	5	out	out	ADP
cana-5102	56	6	to	to	PART
cana-5102	56	7	be	be	AUX
cana-5102	56	8	98.2	98.2	NUM
cana-5102	56	9	%	%	NOUN
cana-5102	56	10	.	.	PUNCT
cana-5102	57	1	here	here	ADV
cana-5102	57	2	each	each	DET
cana-5102	57	3	pixel	pixel	PROPN
cana-5102	57	4	act	act	PROPN
cana-5102	57	5	as	as	ADP
cana-5102	57	6	input	input	NOUN
cana-5102	57	7	neuron	neuron	NOUN
cana-5102	57	8	for	for	ADP
cana-5102	57	9	artificial	artificial	ADJ
cana-5102	57	10	neural	neural	ADJ
cana-5102	57	11	network	network	NOUN
cana-5102	57	12	.	.	PUNCT
cana-5102	58	1	connell	connell	PROPN
cana-5102	58	2	et	et	PROPN
cana-5102	58	3	al	al	PROPN
cana-5102	58	4	.	.	PROPN
cana-5102	58	5	2000	2000	NUM
cana-5102	58	6	proposed	propose	VERB
cana-5102	58	7	to	to	PART
cana-5102	58	8	recognise	recognise	VERB
cana-5102	58	9	hindi	hindi	NOUN
cana-5102	58	10	character	character	NOUN
cana-5102	58	11	written	write	VERB
cana-5102	58	12	on	on	ADP
cana-5102	58	13	screen	screen	NOUN
cana-5102	58	14	through	through	ADP
cana-5102	58	15	pen	pen	NOUN
cana-5102	58	16	.	.	PUNCT
cana-5102	59	1	a	a	DET
cana-5102	59	2	devanagari	devanagari	ADJ
cana-5102	59	3	character	character	NOUN
cana-5102	59	4	experiment	experiment	NOUN
cana-5102	59	5	with	with	ADP
cana-5102	59	6	more	more	ADJ
cana-5102	59	7	than	than	ADP
cana-5102	59	8	20	20	NUM
cana-5102	59	9	different	different	ADJ
cana-5102	59	10	writers	writer	NOUN
cana-5102	59	11	and	and	CCONJ
cana-5102	59	12	each	each	PRON
cana-5102	59	13	giving	give	VERB
cana-5102	59	14	5	5	NUM
cana-5102	59	15	datasets	dataset	NOUN
cana-5102	59	16	is	be	AUX
cana-5102	59	17	conducted	conduct	VERB
cana-5102	59	18	which	which	PRON
cana-5102	59	19	resulted	result	VERB
cana-5102	59	20	in	in	ADP
cana-5102	59	21	86.5	86.5	NUM
cana-5102	59	22	%	%	NOUN
cana-5102	59	23	recognition	recognition	NOUN
cana-5102	59	24	rate	rate	NOUN
cana-5102	59	25	.	.	PUNCT
cana-5102	60	1	here	here	ADV
cana-5102	60	2	both	both	DET
cana-5102	60	3	hidden	hide	VERB
cana-5102	60	4	markov	markov	NOUN
cana-5102	60	5	model	model	NOUN
cana-5102	60	6	(	(	PUNCT
cana-5102	60	7	for	for	ADP
cana-5102	60	8	online	online	ADJ
cana-5102	60	9	points	point	NOUN
cana-5102	60	10	)	)	PUNCT
cana-5102	60	11	and	and	CCONJ
cana-5102	60	12	nn	nn	PROPN
cana-5102	60	13	for	for	ADP
cana-5102	60	14	offline	offline	ADJ
cana-5102	60	15	images	image	NOUN
cana-5102	60	16	are	be	AUX
cana-5102	60	17	used	use	VERB
cana-5102	60	18	for	for	ADP
cana-5102	60	19	recognition.[2	recognition.[2	X
cana-5102	60	20	]	]	X
cana-5102	60	21	n.	n.	PROPN
cana-5102	60	22	arica	arica	PROPN
cana-5102	60	23	et	et	PROPN
cana-5102	60	24	al	al	PROPN
cana-5102	60	25	.	.	PROPN
cana-5102	60	26	2001	2001	NUM
cana-5102	61	1	[	[	X
cana-5102	61	2	3	3	X
cana-5102	61	3	]	]	ADJ
cana-5102	61	4	talks	talk	NOUN
cana-5102	61	5	about	about	ADP
cana-5102	61	6	the	the	DET
cana-5102	61	7	steps	step	NOUN
cana-5102	61	8	taken	take	VERB
cana-5102	61	9	by	by	ADP
cana-5102	61	10	the	the	DET
cana-5102	61	11	character	character	NOUN
cana-5102	61	12	recognition	recognition	NOUN
cana-5102	61	13	process	process	NOUN
cana-5102	61	14	and	and	CCONJ
cana-5102	61	15	their	their	PRON
cana-5102	61	16	different	different	ADJ
cana-5102	61	17	types	type	NOUN
cana-5102	61	18	and	and	CCONJ
cana-5102	61	19	variations	variation	NOUN
cana-5102	61	20	.	.	PUNCT
cana-5102	62	1	[	[	X
cana-5102	62	2	4	4	NUM
cana-5102	62	3	]	]	X
cana-5102	62	4	ramakrishnan	ramakrishnan	PROPN
cana-5102	62	5	n.d	n.d	PROPN
cana-5102	62	6	.	.	PROPN
cana-5102	62	7	2002	2002	NUM
cana-5102	62	8	created	create	VERB
cana-5102	62	9	model	model	NOUN
cana-5102	62	10	which	which	PRON
cana-5102	62	11	supports	support	VERB
cana-5102	62	12	both	both	PRON
cana-5102	62	13	tamil	tamil	NOUN
cana-5102	62	14	and	and	CCONJ
cana-5102	62	15	roman	roman	ADJ
cana-5102	62	16	scripts	script	NOUN
cana-5102	62	17	.	.	PUNCT
cana-5102	63	1	the	the	DET
cana-5102	63	2	accuracy	accuracy	NOUN
cana-5102	63	3	for	for	ADP
cana-5102	63	4	tamil	tamil	PROPN
cana-5102	63	5	,	,	PUNCT
cana-5102	63	6	english	english	PROPN
cana-5102	63	7	in	in	ADP
cana-5102	63	8	svm	svm	PROPN
cana-5102	63	9	is	be	AUX
cana-5102	63	10	88.43	88.43	NUM
cana-5102	63	11	and	and	CCONJ
cana-5102	63	12	87.76	87.76	NUM
cana-5102	63	13	%	%	NOUN
cana-5102	63	14	while	while	SCONJ
cana-5102	63	15	in	in	ADP
cana-5102	63	16	case	case	NOUN
cana-5102	63	17	of	of	ADP
cana-5102	63	18	neural	neural	ADJ
cana-5102	63	19	network	network	NOUN
cana-5102	63	20	it	it	PRON
cana-5102	63	21	is	be	AUX
cana-5102	63	22	73.61	73.61	NUM
cana-5102	63	23	and	and	CCONJ
cana-5102	63	24	71.23	71.23	NUM
cana-5102	63	25	%	%	NOUN
cana-5102	63	26	and	and	CCONJ
cana-5102	63	27	for	for	ADP
cana-5102	63	28	k	k	PROPN
cana-5102	63	29	-	-	PROPN
cana-5102	63	30	nn	nn	ADJ
cana-5102	63	31	it	it	PRON
cana-5102	63	32	i	i	PRON
cana-5102	63	33	68.25	68.25	NUM
cana-5102	63	34	and	and	CCONJ
cana-5102	63	35	84.72	84.72	NUM
cana-5102	63	36	%	%	NOUN
cana-5102	63	37	respectively	respectively	ADV
cana-5102	63	38	.	.	PUNCT
cana-5102	64	1	ashwin	ashwin	PROPN
cana-5102	64	2	et	et	PROPN
cana-5102	64	3	al	al	PROPN
cana-5102	64	4	.	.	PROPN
cana-5102	64	5	2002	2002	NUM
cana-5102	65	1	[	[	X
cana-5102	65	2	5	5	X
cana-5102	65	3	]	]	PUNCT
cana-5102	65	4	provide	provide	VERB
cana-5102	65	5	a	a	DET
cana-5102	65	6	fresh	fresh	ADJ
cana-5102	65	7	set	set	NOUN
cana-5102	65	8	of	of	ADP
cana-5102	65	9	computationally	computationally	ADV
cana-5102	65	10	straightforward	straightforward	ADJ
cana-5102	65	11	features	feature	NOUN
cana-5102	65	12	for	for	ADP
cana-5102	65	13	the	the	DET
cana-5102	65	14	recognition	recognition	NOUN
cana-5102	65	15	problem	problem	NOUN
cana-5102	65	16	.	.	PUNCT
cana-5102	66	1	to	to	PART
cana-5102	66	2	obtain	obtain	VERB
cana-5102	66	3	the	the	DET
cana-5102	66	4	final	final	ADJ
cana-5102	66	5	recognition	recognition	NOUN
cana-5102	66	6	,	,	PUNCT
cana-5102	66	7	several	several	ADJ
cana-5102	66	8	2	2	NUM
cana-5102	66	9	-	-	PUNCT
cana-5102	66	10	class	class	NOUN
cana-5102	66	11	classifiers	classifier	NOUN
cana-5102	66	12	were	be	AUX
cana-5102	66	13	used	use	VERB
cana-5102	66	14	utilizing	utilize	VERB
cana-5102	66	15	the	the	DET
cana-5102	66	16	support	support	NOUN
cana-5102	66	17	vector	vector	NOUN
cana-5102	66	18	machine	machine	NOUN
cana-5102	66	19	(	(	PUNCT
cana-5102	66	20	svm	svm	ADJ
cana-5102	66	21	)	)	PUNCT
cana-5102	66	22	technique	technique	NOUN
cana-5102	66	23	.	.	PUNCT
cana-5102	67	1	arica	arica	PROPN
cana-5102	67	2	et	et	PROPN
cana-5102	67	3	al	al	PROPN
cana-5102	67	4	.	.	PROPN
cana-5102	67	5	2002	2002	NUM
cana-5102	68	1	[	[	X
cana-5102	68	2	6	6	NUM
cana-5102	68	3	]	]	PUNCT
cana-5102	68	4	provided	provide	VERB
cana-5102	68	5	a	a	DET
cana-5102	68	6	novel	novel	ADJ
cana-5102	68	7	analytical	analytical	ADJ
cana-5102	68	8	framework	framework	NOUN
cana-5102	68	9	for	for	ADP
cana-5102	68	10	the	the	DET
cana-5102	68	11	offline	offline	ADJ
cana-5102	68	12	recognition	recognition	NOUN
cana-5102	68	13	of	of	ADP
cana-5102	68	14	cursive	cursive	ADJ
cana-5102	68	15	handwriting	handwriting	NOUN
cana-5102	68	16	.	.	PUNCT
cana-5102	69	1	slant	slant	ADJ
cana-5102	69	2	angle	angle	NOUN
cana-5102	69	3	,	,	PUNCT
cana-5102	69	4	stock	stock	NOUN
cana-5102	69	5	height	height	NOUN
cana-5102	69	6	and	and	CCONJ
cana-5102	69	7	width	width	ADJ
cana-5102	69	8	,	,	PUNCT
cana-5102	69	9	baseline	baseline	PROPN
cana-5102	69	10	are	be	AUX
cana-5102	69	11	used	use	VERB
cana-5102	69	12	for	for	ADP
cana-5102	69	13	feature	feature	NOUN
cana-5102	69	14	communications	communication	NOUN
cana-5102	69	15	on	on	ADP
cana-5102	69	16	applied	apply	VERB
cana-5102	69	17	nonlinear	nonlinear	ADJ
cana-5102	69	18	analysis	analysis	NOUN
cana-5102	69	19	issn	issn	NOUN
cana-5102	69	20	:	:	PUNCT
cana-5102	69	21	1074	1074	NUM
cana-5102	69	22	-	-	PUNCT
cana-5102	69	23	133x	133x	NUM
cana-5102	69	24	vol	vol	NOUN
cana-5102	69	25	32	32	NUM
cana-5102	69	26	no	no	NOUN
cana-5102	69	27	.	.	PUNCT
cana-5102	70	1	icmasd	icmasd	NOUN
cana-5102	70	2	(	(	PUNCT
cana-5102	70	3	2025	2025	NUM
cana-5102	70	4	)	)	PUNCT
cana-5102	70	5	758	758	NUM
cana-5102	70	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	70	7	extraction	extraction	NOUN
cana-5102	70	8	.	.	PUNCT
cana-5102	71	1	third	third	ADJ
cana-5102	71	2	,	,	PUNCT
cana-5102	71	3	character	character	NOUN
cana-5102	71	4	candidates	candidate	NOUN
cana-5102	71	5	are	be	AUX
cana-5102	71	6	labelled	label	VERB
cana-5102	71	7	and	and	CCONJ
cana-5102	71	8	ranked	rank	VERB
cana-5102	71	9	using	use	VERB
cana-5102	71	10	hidden	hide	VERB
cana-5102	71	11	markov	markov	NOUN
cana-5102	71	12	models	model	NOUN
cana-5102	71	13	(	(	PUNCT
cana-5102	71	14	hmm	hmm	INTJ
cana-5102	71	15	)	)	PUNCT
cana-5102	71	16	for	for	ADP
cana-5102	71	17	shape	shape	NOUN
cana-5102	71	18	identification	identification	NOUN
cana-5102	71	19	.	.	PUNCT
cana-5102	72	1	different	different	ADJ
cana-5102	72	2	word	word	NOUN
cana-5102	72	3	sizes	size	NOUN
cana-5102	72	4	have	have	VERB
cana-5102	72	5	different	different	ADJ
cana-5102	72	6	recognition	recognition	NOUN
cana-5102	72	7	rates	rate	NOUN
cana-5102	72	8	in	in	ADP
cana-5102	72	9	the	the	DET
cana-5102	72	10	entire	entire	ADJ
cana-5102	72	11	testing	testing	NOUN
cana-5102	72	12	space	space	NOUN
cana-5102	72	13	.	.	PUNCT
cana-5102	73	1	this	this	PRON
cana-5102	73	2	results	result	VERB
cana-5102	73	3	in	in	ADP
cana-5102	73	4	88.3	88.3	NUM
cana-5102	73	5	%	%	NOUN
cana-5102	73	6	accuracy	accuracy	NOUN
cana-5102	73	7	with	with	ADP
cana-5102	73	8	30,000	30,000	NUM
cana-5102	73	9	words	word	NOUN
cana-5102	73	10	.	.	PUNCT
cana-5102	74	1	bajaj	bajaj	PROPN
cana-5102	74	2	et	et	PROPN
cana-5102	74	3	al	al	PROPN
cana-5102	74	4	.	.	PROPN
cana-5102	74	5	2002	2002	NUM
cana-5102	75	1	[	[	X
cana-5102	75	2	7	7	X
cana-5102	75	3	]	]	PUNCT
cana-5102	75	4	proposed	propose	VERB
cana-5102	75	5	to	to	PART
cana-5102	75	6	classify	classify	VERB
cana-5102	75	7	devanagari	devanagari	PROPN
cana-5102	75	8	numerical	numerical	ADJ
cana-5102	75	9	through	through	ADP
cana-5102	75	10	multi	multi	ADJ
cana-5102	75	11	-	-	NOUN
cana-5102	75	12	classifier	classifier	NOUN
cana-5102	75	13	that	that	PRON
cana-5102	75	14	have	have	VERB
cana-5102	75	15	different	different	ADJ
cana-5102	75	16	type	type	NOUN
cana-5102	75	17	of	of	ADP
cana-5102	75	18	features	feature	NOUN
cana-5102	75	19	to	to	PART
cana-5102	75	20	classify	classify	VERB
cana-5102	75	21	.	.	PUNCT
cana-5102	76	1	this	this	DET
cana-5102	76	2	paper	paper	NOUN
cana-5102	76	3	uses	use	VERB
cana-5102	76	4	density	density	NOUN
cana-5102	76	5	feature	feature	NOUN
cana-5102	76	6	,	,	PUNCT
cana-5102	76	7	moment	moment	VERB
cana-5102	76	8	feature	feature	NOUN
cana-5102	76	9	of	of	ADP
cana-5102	76	10	4	4	NUM
cana-5102	76	11	directional	directional	ADJ
cana-5102	76	12	curves	curve	NOUN
cana-5102	76	13	and	and	CCONJ
cana-5102	76	14	descriptive	descriptive	ADJ
cana-5102	76	15	component	component	NOUN
cana-5102	76	16	feature	feature	NOUN
cana-5102	76	17	to	to	PART
cana-5102	76	18	abstract	abstract	VERB
cana-5102	76	19	the	the	DET
cana-5102	76	20	information	information	NOUN
cana-5102	76	21	about	about	ADP
cana-5102	76	22	the	the	DET
cana-5102	76	23	image	image	NOUN
cana-5102	76	24	.	.	PUNCT
cana-5102	77	1	a	a	DET
cana-5102	77	2	3	3	NUM
cana-5102	77	3	-	-	PUNCT
cana-5102	77	4	layer	layer	NOUN
cana-5102	77	5	multilayer	multilayer	NOUN
cana-5102	77	6	perceptron	perceptron	PROPN
cana-5102	77	7	is	be	AUX
cana-5102	77	8	used	use	VERB
cana-5102	77	9	with	with	ADP
cana-5102	77	10	knn	knn	PROPN
cana-5102	77	11	at	at	ADP
cana-5102	77	12	its	its	PRON
cana-5102	77	13	base	base	NOUN
cana-5102	77	14	layer	layer	NOUN
cana-5102	77	15	with	with	ADP
cana-5102	77	16	back	back	ADJ
cana-5102	77	17	propagation	propagation	NOUN
cana-5102	77	18	for	for	ADP
cana-5102	77	19	classification	classification	NOUN
cana-5102	77	20	.	.	PUNCT
cana-5102	78	1	the	the	DET
cana-5102	78	2	model	model	NOUN
cana-5102	78	3	is	be	AUX
cana-5102	78	4	tested	test	VERB
cana-5102	78	5	with	with	ADP
cana-5102	78	6	2460	2460	NUM
cana-5102	78	7	samples	sample	NOUN
cana-5102	78	8	and	and	CCONJ
cana-5102	78	9	accuracy	accuracy	NOUN
cana-5102	78	10	is	be	AUX
cana-5102	78	11	89.68	89.68	NUM
cana-5102	78	12	percent	percent	NOUN
cana-5102	78	13	.	.	PUNCT
cana-5102	79	1	cowell	cowell	VERB
cana-5102	79	2	et	et	PROPN
cana-5102	79	3	al	al	PROPN
cana-5102	79	4	.	.	PROPN
cana-5102	79	5	2003	2003	NUM
cana-5102	80	1	[	[	X
cana-5102	80	2	8	8	NUM
cana-5102	80	3	]	]	PUNCT
cana-5102	80	4	highlighted	highlight	VERB
cana-5102	80	5	the	the	DET
cana-5102	80	6	challenges	challenge	NOUN
cana-5102	80	7	of	of	ADP
cana-5102	80	8	using	use	VERB
cana-5102	80	9	standard	standard	ADJ
cana-5102	80	10	structural	structural	ADJ
cana-5102	80	11	and	and	CCONJ
cana-5102	80	12	syntactic	syntactic	ADJ
cana-5102	80	13	recognition	recognition	NOUN
cana-5102	80	14	algorithms	algorithm	NOUN
cana-5102	80	15	when	when	SCONJ
cana-5102	80	16	describing	describe	VERB
cana-5102	80	17	the	the	DET
cana-5102	80	18	amharic	amharic	PROPN
cana-5102	80	19	script	script	NOUN
cana-5102	80	20	.	.	PUNCT
cana-5102	81	1	first	first	ADV
cana-5102	81	2	the	the	DET
cana-5102	81	3	character	character	NOUN
cana-5102	81	4	is	be	AUX
cana-5102	81	5	measured	measure	VERB
cana-5102	81	6	against	against	ADP
cana-5102	81	7	several	several	ADJ
cana-5102	81	8	templates	template	NOUN
cana-5102	81	9	and	and	CCONJ
cana-5102	81	10	compares	compare	VERB
cana-5102	81	11	it	it	PRON
cana-5102	81	12	to	to	ADP
cana-5102	81	13	a	a	DET
cana-5102	81	14	set	set	NOUN
cana-5102	81	15	of	of	ADP
cana-5102	81	16	template	template	NOUN
cana-5102	81	17	signatures	signature	NOUN
cana-5102	81	18	.	.	PUNCT
cana-5102	82	1	377	377	NUM
cana-5102	82	2	pairings	pairing	NOUN
cana-5102	82	3	have	have	VERB
cana-5102	82	4	ratings	rating	NOUN
cana-5102	82	5	of	of	ADP
cana-5102	82	6	90	90	NUM
cana-5102	82	7	or	or	CCONJ
cana-5102	82	8	above	above	ADJ
cana-5102	82	9	.	.	PUNCT
cana-5102	83	1	in	in	ADP
cana-5102	83	2	addition	addition	NOUN
cana-5102	83	3	,	,	PUNCT
cana-5102	83	4	two	two	NUM
cana-5102	83	5	pairs	pair	NOUN
cana-5102	83	6	have	have	VERB
cana-5102	83	7	ratings	rating	NOUN
cana-5102	83	8	of	of	ADP
cana-5102	83	9	99	99	NUM
cana-5102	83	10	and	and	CCONJ
cana-5102	83	11	23	23	NUM
cana-5102	83	12	pairs	pair	NOUN
cana-5102	83	13	have	have	VERB
cana-5102	83	14	ratings	rating	NOUN
cana-5102	83	15	of	of	ADP
cana-5102	83	16	98	98	NUM
cana-5102	83	17	.	.	PUNCT
cana-5102	84	1	noor	noor	PROPN
cana-5102	84	2	et	et	PROPN
cana-5102	84	3	al	al	PROPN
cana-5102	84	4	.	.	PROPN
cana-5102	84	5	2004	2004	NUM
cana-5102	85	1	[	[	X
cana-5102	85	2	9	9	X
cana-5102	85	3	]	]	PUNCT
cana-5102	85	4	address	address	NOUN
cana-5102	85	5	the	the	DET
cana-5102	85	6	issue	issue	NOUN
cana-5102	85	7	of	of	ADP
cana-5102	85	8	character	character	NOUN
cana-5102	85	9	rotation	rotation	NOUN
cana-5102	85	10	while	while	SCONJ
cana-5102	85	11	recognition	recognition	NOUN
cana-5102	85	12	of	of	ADP
cana-5102	85	13	english	english	ADJ
cana-5102	85	14	characters	character	NOUN
cana-5102	85	15	.	.	PUNCT
cana-5102	86	1	with	with	ADP
cana-5102	86	2	more	more	ADJ
cana-5102	86	3	than	than	ADP
cana-5102	86	4	76	76	NUM
cana-5102	86	5	%	%	NOUN
cana-5102	86	6	accuracy	accuracy	NOUN
cana-5102	86	7	for	for	ADP
cana-5102	86	8	both	both	DET
cana-5102	86	9	classifiers	classifier	NOUN
cana-5102	86	10	in	in	ADP
cana-5102	86	11	the	the	DET
cana-5102	86	12	chosen	choose	VERB
cana-5102	86	13	test	test	NOUN
cana-5102	86	14	set	set	VERB
cana-5102	86	15	.	.	PUNCT
cana-5102	87	1	it	it	PRON
cana-5102	87	2	results	result	VERB
cana-5102	87	3	in	in	ADP
cana-5102	87	4	more	more	ADJ
cana-5102	87	5	than	than	ADP
cana-5102	87	6	90	90	NUM
cana-5102	87	7	%	%	NOUN
cana-5102	87	8	accuracy	accuracy	NOUN
cana-5102	87	9	with	with	ADP
cana-5102	87	10	some	some	DET
cana-5102	87	11	characters	character	NOUN
cana-5102	87	12	showing	show	VERB
cana-5102	87	13	low	low	ADJ
cana-5102	87	14	accuracy	accuracy	NOUN
cana-5102	87	15	while	while	SCONJ
cana-5102	87	16	rotated	rotate	VERB
cana-5102	87	17	like	like	ADP
cana-5102	87	18	“	"	PUNCT
cana-5102	87	19	h	h	NOUN
cana-5102	87	20	”	"	PUNCT
cana-5102	87	21	,	,	PUNCT
cana-5102	87	22	“	"	PUNCT
cana-5102	87	23	m	m	INTJ
cana-5102	87	24	”	"	PUNCT
cana-5102	87	25	,	,	PUNCT
cana-5102	87	26	“	"	PUNCT
cana-5102	87	27	w	w	NOUN
cana-5102	87	28	”	"	PUNCT
cana-5102	87	29	etc	etc	X
cana-5102	87	30	.	.	X
cana-5102	88	1	fukumoto	fukumoto	PRON
cana-5102	88	2	et	et	PROPN
cana-5102	88	3	al	al	PROPN
cana-5102	88	4	.	.	PROPN
cana-5102	88	5	2004	2004	NUM
cana-5102	89	1	[	[	X
cana-5102	89	2	10	10	NUM
cana-5102	89	3	]	]	PUNCT
cana-5102	89	4	focuses	focus	VERB
cana-5102	89	5	on	on	ADP
cana-5102	89	6	improving	improve	VERB
cana-5102	89	7	the	the	DET
cana-5102	89	8	glvq	glvq	NOUN
cana-5102	89	9	's	's	PART
cana-5102	89	10	ability	ability	NOUN
cana-5102	89	11	to	to	PART
cana-5102	89	12	recognize	recognize	VERB
cana-5102	89	13	handwritten	handwritten	ADJ
cana-5102	89	14	characters	character	NOUN
cana-5102	89	15	with	with	ADP
cana-5102	89	16	greater	great	ADJ
cana-5102	89	17	precision	precision	NOUN
cana-5102	89	18	(	(	PUNCT
cana-5102	89	19	generalized	generalized	ADJ
cana-5102	89	20	learning	learn	VERB
cana-5102	89	21	vector	vector	NOUN
cana-5102	89	22	quantization	quantization	NOUN
cana-5102	89	23	)	)	PUNCT
cana-5102	89	24	.	.	PUNCT
cana-5102	90	1	fda	fda	PROPN
cana-5102	90	2	is	be	AUX
cana-5102	90	3	combined	combine	VERB
cana-5102	90	4	with	with	ADP
cana-5102	90	5	glvq	glvq	NOUN
cana-5102	90	6	in	in	ADP
cana-5102	90	7	previous	previous	ADJ
cana-5102	90	8	paper	paper	NOUN
cana-5102	90	9	to	to	PART
cana-5102	90	10	identify	identify	VERB
cana-5102	90	11	the	the	DET
cana-5102	90	12	handwritten	handwritten	ADJ
cana-5102	90	13	chinese	chinese	ADJ
cana-5102	90	14	character	character	NOUN
cana-5102	90	15	using	use	VERB
cana-5102	90	16	euclidean	euclidean	ADJ
cana-5102	90	17	distance	distance	NOUN
cana-5102	90	18	which	which	PRON
cana-5102	90	19	are	be	AUX
cana-5102	90	20	gated	gate	VERB
cana-5102	90	21	and	and	CCONJ
cana-5102	90	22	evaluated	evaluate	VERB
cana-5102	90	23	to	to	PART
cana-5102	90	24	be	be	AUX
cana-5102	90	25	very	very	ADV
cana-5102	90	26	effective	effective	ADJ
cana-5102	90	27	.	.	PUNCT
cana-5102	91	1	the	the	DET
cana-5102	91	2	accuracy	accuracy	NOUN
cana-5102	91	3	increases	increase	VERB
cana-5102	91	4	to	to	ADP
cana-5102	91	5	99.6	99.6	NUM
cana-5102	91	6	%	%	NOUN
cana-5102	91	7	for	for	ADP
cana-5102	91	8	gvq	gvq	PROPN
cana-5102	91	9	,	,	PUNCT
cana-5102	91	10	fd	fd	PUNCT
cana-5102	91	11	and	and	CCONJ
cana-5102	91	12	projectile	projectile	NOUN
cana-5102	91	13	distance	distance	NOUN
cana-5102	91	14	used	use	VERB
cana-5102	91	15	all	all	ADV
cana-5102	91	16	together	together	ADV
cana-5102	91	17	.	.	PUNCT
cana-5102	92	1	sharma	sharma	PROPN
cana-5102	92	2	et	et	PROPN
cana-5102	92	3	al	al	PROPN
cana-5102	92	4	.	.	PROPN
cana-5102	92	5	2006	2006	NUM
cana-5102	93	1	[	[	X
cana-5102	93	2	11	11	NUM
cana-5102	93	3	]	]	PUNCT
cana-5102	93	4	proposed	propose	VERB
cana-5102	93	5	a	a	DET
cana-5102	93	6	quadric	quadric	ADJ
cana-5102	93	7	classifier	classifier	NOUN
cana-5102	93	8	for	for	ADP
cana-5102	93	9	offline	offline	ADJ
cana-5102	93	10	devanagari	devanagari	ADJ
cana-5102	93	11	character	character	NOUN
cana-5102	93	12	recognition	recognition	NOUN
cana-5102	93	13	.	.	PUNCT
cana-5102	94	1	direction	direction	NOUN
cana-5102	94	2	chain	chain	NOUN
cana-5102	94	3	code	code	NOUN
cana-5102	94	4	is	be	AUX
cana-5102	94	5	used	use	VERB
cana-5102	94	6	which	which	PRON
cana-5102	94	7	gives	give	VERB
cana-5102	94	8	98.86	98.86	NUM
cana-5102	94	9	%	%	NOUN
cana-5102	94	10	and	and	CCONJ
cana-5102	94	11	80.36	80.36	NUM
cana-5102	94	12	%	%	NOUN
cana-5102	94	13	accuracy	accuracy	NOUN
cana-5102	94	14	for	for	ADP
cana-5102	94	15	numerals	numeral	NOUN
cana-5102	94	16	and	and	CCONJ
cana-5102	94	17	characters	character	NOUN
cana-5102	94	18	respectively	respectively	ADV
cana-5102	94	19	.	.	PUNCT
cana-5102	95	1	hanmandlu	hanmandlu	PROPN
cana-5102	95	2	et	et	PROPN
cana-5102	95	3	al	al	PROPN
cana-5102	95	4	.	.	PROPN
cana-5102	95	5	2007	2007	NUM
cana-5102	96	1	[	[	X
cana-5102	96	2	12	12	NUM
cana-5102	96	3	]	]	PUNCT
cana-5102	96	4	recognized	recognize	VERB
cana-5102	96	5	hindi	hindi	NOUN
cana-5102	96	6	characters	character	NOUN
cana-5102	96	7	using	use	VERB
cana-5102	96	8	normalized	normalize	VERB
cana-5102	96	9	distances	distance	NOUN
cana-5102	96	10	as	as	ADP
cana-5102	96	11	feature	feature	NOUN
cana-5102	96	12	and	and	CCONJ
cana-5102	96	13	exponential	exponential	ADJ
cana-5102	96	14	function	function	NOUN
cana-5102	96	15	fitted	fit	VERB
cana-5102	96	16	fuzzy	fuzzy	ADJ
cana-5102	96	17	network	network	NOUN
cana-5102	96	18	to	to	PART
cana-5102	96	19	classify	classify	VERB
cana-5102	96	20	the	the	DET
cana-5102	96	21	characters	character	NOUN
cana-5102	96	22	.	.	PUNCT
cana-5102	97	1	reuse	reuse	NOUN
cana-5102	97	2	policy	policy	NOUN
cana-5102	97	3	is	be	AUX
cana-5102	97	4	used	use	VERB
cana-5102	97	5	under	under	ADP
cana-5102	97	6	reinforcement	reinforcement	NOUN
cana-5102	97	7	learning	learning	NOUN
cana-5102	97	8	which	which	PRON
cana-5102	97	9	increases	increase	VERB
cana-5102	97	10	accuracy	accuracy	NOUN
cana-5102	97	11	by	by	ADP
cana-5102	97	12	25	25	NUM
cana-5102	97	13	times	time	NOUN
cana-5102	97	14	when	when	SCONJ
cana-5102	97	15	learning	learn	VERB
cana-5102	97	16	.	.	PUNCT
cana-5102	98	1	4750	4750	NUM
cana-5102	98	2	samples	sample	NOUN
cana-5102	98	3	give	give	VERB
cana-5102	98	4	an	an	DET
cana-5102	98	5	accuracy	accuracy	NOUN
cana-5102	98	6	of	of	ADP
cana-5102	98	7	90.65	90.65	NUM
cana-5102	98	8	%	%	NOUN
cana-5102	98	9	.	.	PUNCT
cana-5102	99	1	pal	pal	PROPN
cana-5102	99	2	et	et	PROPN
cana-5102	99	3	al	al	PROPN
cana-5102	99	4	.	.	PROPN
cana-5102	99	5	2007	2007	NUM
cana-5102	100	1	[	[	X
cana-5102	100	2	13	13	NUM
cana-5102	100	3	]	]	PUNCT
cana-5102	100	4	tries	try	VERB
cana-5102	100	5	to	to	PART
cana-5102	100	6	recognize	recognize	VERB
cana-5102	100	7	the	the	DET
cana-5102	100	8	numeric	numeric	NOUN
cana-5102	100	9	in	in	ADP
cana-5102	100	10	indic	indic	ADJ
cana-5102	100	11	version	version	NOUN
cana-5102	100	12	using	use	VERB
cana-5102	100	13	quadric	quadric	ADJ
cana-5102	100	14	classifier	classifier	NOUN
cana-5102	100	15	.	.	PUNCT
cana-5102	101	1	the	the	DET
cana-5102	101	2	images	image	NOUN
cana-5102	101	3	are	be	AUX
cana-5102	101	4	first	first	ADV
cana-5102	101	5	passed	pass	VERB
cana-5102	101	6	through	through	ADP
cana-5102	101	7	directional	directional	ADJ
cana-5102	101	8	chain	chain	NOUN
cana-5102	101	9	code	code	NOUN
cana-5102	101	10	which	which	PRON
cana-5102	101	11	then	then	ADV
cana-5102	101	12	goes	go	VERB
cana-5102	101	13	through	through	ADP
cana-5102	101	14	gaussian	gaussian	ADJ
cana-5102	101	15	filter	filter	NOUN
cana-5102	101	16	which	which	PRON
cana-5102	101	17	result	result	VERB
cana-5102	101	18	in	in	ADP
cana-5102	101	19	the	the	DET
cana-5102	101	20	features	feature	NOUN
cana-5102	101	21	extracted	extract	VERB
cana-5102	101	22	which	which	PRON
cana-5102	101	23	result	result	VERB
cana-5102	101	24	in	in	ADP
cana-5102	101	25	99.56	99.56	NUM
cana-5102	101	26	%	%	NOUN
cana-5102	101	27	accuracy	accuracy	NOUN
cana-5102	101	28	.	.	PUNCT
cana-5102	102	1	pal	pal	PROPN
cana-5102	102	2	et	et	PROPN
cana-5102	102	3	al	al	PROPN
cana-5102	102	4	.	.	PROPN
cana-5102	102	5	2008	2008	NUM
cana-5102	103	1	[	[	X
cana-5102	103	2	14	14	NUM
cana-5102	103	3	]	]	PUNCT
cana-5102	103	4	proposed	propose	VERB
cana-5102	103	5	to	to	PART
cana-5102	103	6	increase	increase	VERB
cana-5102	103	7	the	the	DET
cana-5102	103	8	accuracy	accuracy	NOUN
cana-5102	103	9	of	of	ADP
cana-5102	103	10	devanagari	devanagari	ADJ
cana-5102	103	11	character	character	NOUN
cana-5102	103	12	by	by	ADP
cana-5102	103	13	using	use	VERB
cana-5102	103	14	directional	directional	ADJ
cana-5102	103	15	feature	feature	NOUN
cana-5102	103	16	and	and	CCONJ
cana-5102	103	17	curvature	curvature	NOUN
cana-5102	103	18	feature	feature	NOUN
cana-5102	103	19	and	and	CCONJ
cana-5102	103	20	for	for	ADP
cana-5102	103	21	classification	classification	NOUN
cana-5102	103	22	svm	svm	NOUN
cana-5102	103	23	and	and	CCONJ
cana-5102	103	24	mqdf	mqdf	NOUN
cana-5102	103	25	are	be	AUX
cana-5102	103	26	used	use	VERB
cana-5102	103	27	with	with	ADP
cana-5102	103	28	36172	36172	NUM
cana-5102	103	29	dataset	dataset	VERB
cana-5102	103	30	and	and	CCONJ
cana-5102	103	31	got	get	VERB
cana-5102	103	32	95.13	95.13	NUM
cana-5102	103	33	%	%	NOUN
cana-5102	103	34	accuracy	accuracy	NOUN
cana-5102	103	35	.	.	PUNCT
cana-5102	104	1	we	we	PRON
cana-5102	104	2	saw	see	VERB
cana-5102	104	3	that	that	SCONJ
cana-5102	104	4	different	different	ADJ
cana-5102	104	5	feature	feature	NOUN
cana-5102	104	6	extraction	extraction	NOUN
cana-5102	104	7	techniques	technique	NOUN
cana-5102	104	8	are	be	AUX
cana-5102	104	9	used	use	VERB
cana-5102	104	10	for	for	ADP
cana-5102	104	11	better	well	ADJ
cana-5102	104	12	accuracy	accuracy	NOUN
cana-5102	104	13	.	.	PUNCT
cana-5102	105	1	mali	mali	PROPN
cana-5102	105	2	et	et	PROPN
cana-5102	105	3	al	al	PROPN
cana-5102	105	4	.	.	PROPN
cana-5102	105	5	2010	2010	NUM
cana-5102	106	1	[	[	X
cana-5102	106	2	15	15	NUM
cana-5102	106	3	]	]	PUNCT
cana-5102	106	4	proposed	propose	VERB
cana-5102	106	5	to	to	PART
cana-5102	106	6	classify	classify	VERB
cana-5102	106	7	individual	individual	ADJ
cana-5102	106	8	marathi	marathi	PROPN
cana-5102	106	9	numerals	numeral	NOUN
cana-5102	106	10	written	write	VERB
cana-5102	106	11	in	in	ADP
cana-5102	106	12	devanagari	devanagari	ADJ
cana-5102	106	13	script	script	NOUN
cana-5102	106	14	where	where	SCONJ
cana-5102	106	15	64	64	NUM
cana-5102	106	16	fourier	fourier	NOUN
cana-5102	106	17	descriptor	descriptor	NOUN
cana-5102	106	18	are	be	AUX
cana-5102	106	19	used	use	VERB
cana-5102	106	20	to	to	PART
cana-5102	106	21	describe	describe	VERB
cana-5102	106	22	the	the	DET
cana-5102	106	23	shape	shape	NOUN
cana-5102	106	24	of	of	ADP
cana-5102	106	25	characters	character	NOUN
cana-5102	106	26	.	.	PUNCT
cana-5102	107	1	3	3	NUM
cana-5102	107	2	classifiers	classifier	NOUN
cana-5102	107	3	nn	nn	PROPN
cana-5102	107	4	,	,	PUNCT
cana-5102	107	5	knn	knn	PROPN
cana-5102	107	6	and	and	CCONJ
cana-5102	107	7	svm	svm	PROPN
cana-5102	107	8	are	be	AUX
cana-5102	107	9	used	use	VERB
cana-5102	107	10	as	as	ADP
cana-5102	107	11	classification	classification	NOUN
cana-5102	107	12	method	method	NOUN
cana-5102	107	13	which	which	PRON
cana-5102	107	14	result	result	VERB
cana-5102	107	15	in	in	ADP
cana-5102	107	16	97.05	97.05	NUM
cana-5102	107	17	%	%	NOUN
cana-5102	107	18	,	,	PUNCT
cana-5102	107	19	97.04%and	97.04%and	NUM
cana-5102	107	20	97.85	97.85	NUM
cana-5102	107	21	%	%	NOUN
cana-5102	107	22	with	with	ADP
cana-5102	107	23	13000	13000	NUM
cana-5102	107	24	samples	sample	NOUN
cana-5102	107	25	.	.	PUNCT
cana-5102	108	1	pal	pal	NOUN
cana-5102	108	2	et	et	PROPN
cana-5102	108	3	al	al	PROPN
cana-5102	108	4	.	.	PROPN
cana-5102	108	5	n.d	n.d	PROPN
cana-5102	108	6	.	.	PROPN
cana-5102	108	7	2010	2010	NUM
cana-5102	109	1	[	[	X
cana-5102	109	2	16	16	NUM
cana-5102	109	3	]	]	PUNCT
cana-5102	109	4	used	use	VERB
cana-5102	109	5	a	a	DET
cana-5102	109	6	multilayer	multilayer	ADJ
cana-5102	109	7	perceptron	perceptron	NOUN
cana-5102	109	8	with	with	ADP
cana-5102	109	9	a	a	DET
cana-5102	109	10	single	single	ADJ
cana-5102	109	11	hidden	hide	VERB
cana-5102	109	12	layer	layer	NOUN
cana-5102	109	13	to	to	PART
cana-5102	109	14	identify	identify	VERB
cana-5102	109	15	handwritten	handwritten	ADJ
cana-5102	109	16	english	english	ADJ
cana-5102	109	17	characters	character	NOUN
cana-5102	109	18	.	.	PUNCT
cana-5102	110	1	boundary	boundary	ADJ
cana-5102	110	2	tracing	tracing	NOUN
cana-5102	110	3	and	and	CCONJ
cana-5102	110	4	the	the	DET
cana-5102	110	5	fourier	fourier	NOUN
cana-5102	110	6	descriptor	descriptor	NOUN
cana-5102	110	7	are	be	AUX
cana-5102	110	8	the	the	DET
cana-5102	110	9	features	feature	NOUN
cana-5102	110	10	taken	take	VERB
cana-5102	110	11	from	from	ADP
cana-5102	110	12	the	the	DET
cana-5102	110	13	handwritten	handwritten	ADJ
cana-5102	110	14	character.500	character.500	NOUN
cana-5102	110	15	samples	sample	NOUN
cana-5102	110	16	were	be	AUX
cana-5102	110	17	used	use	VERB
cana-5102	110	18	for	for	ADP
cana-5102	110	19	the	the	DET
cana-5102	110	20	test	test	NOUN
cana-5102	110	21	with	with	ADP
cana-5102	110	22	a	a	DET
cana-5102	110	23	backpropagation	backpropagation	NOUN
cana-5102	110	24	network	network	NOUN
cana-5102	110	25	can	can	AUX
cana-5102	110	26	recognize	recognize	VERB
cana-5102	110	27	handwritten	handwritten	ADJ
cana-5102	110	28	english	english	ADJ
cana-5102	110	29	letters	letter	NOUN
cana-5102	110	30	with	with	ADP
cana-5102	110	31	an	an	DET
cana-5102	110	32	accuracy	accuracy	NOUN
cana-5102	110	33	of	of	ADP
cana-5102	110	34	94%.nasien	94%.nasien	NUM
cana-5102	110	35	et	et	NOUN
cana-5102	110	36	al	al	PROPN
cana-5102	110	37	.	.	PROPN
cana-5102	110	38	2010	2010	NUM
cana-5102	110	39	proposes	propose	VERB
cana-5102	110	40	using	use	VERB
cana-5102	110	41	the	the	DET
cana-5102	110	42	"	"	PUNCT
cana-5102	110	43	freeman	freeman	PROPN
cana-5102	110	44	chain	chain	NOUN
cana-5102	110	45	code	code	PROPN
cana-5102	110	46	(	(	PUNCT
cana-5102	110	47	fcc	fcc	PROPN
cana-5102	110	48	)	)	PUNCT
cana-5102	110	49	"	"	PUNCT
cana-5102	110	50	to	to	PART
cana-5102	110	51	represent	represent	VERB
cana-5102	110	52	an	an	DET
cana-5102	110	53	image	image	NOUN
cana-5102	110	54	character	character	NOUN
cana-5102	110	55	in	in	ADP
cana-5102	110	56	a	a	DET
cana-5102	110	57	recognition	recognition	NOUN
cana-5102	110	58	model	model	NOUN
cana-5102	110	59	for	for	ADP
cana-5102	110	60	recognizing	recognize	VERB
cana-5102	110	61	english	english	ADJ
cana-5102	110	62	handwritten	handwritten	ADJ
cana-5102	110	63	characters	character	NOUN
cana-5102	110	64	.	.	PUNCT
cana-5102	111	1	chain	chain	NOUN
cana-5102	111	2	codes	code	NOUN
cana-5102	111	3	give	give	VERB
cana-5102	111	4	a	a	DET
cana-5102	111	5	way	way	NOUN
cana-5102	111	6	to	to	PART
cana-5102	111	7	define	define	VERB
cana-5102	111	8	the	the	DET
cana-5102	111	9	bounds	bound	NOUN
cana-5102	111	10	of	of	ADP
cana-5102	111	11	a	a	DET
cana-5102	111	12	character	character	NOUN
cana-5102	111	13	picture	picture	NOUN
cana-5102	111	14	by	by	ADP
cana-5102	111	15	indicating	indicate	VERB
cana-5102	111	16	the	the	DET
cana-5102	111	17	location	location	NOUN
cana-5102	111	18	in	in	ADP
cana-5102	111	19	which	which	PRON
cana-5102	111	20	the	the	DET
cana-5102	111	21	preceding	precede	VERB
cana-5102	111	22	pixel	pixel	NOUN
cana-5102	111	23	will	will	AUX
cana-5102	111	24	be	be	AUX
cana-5102	111	25	positioned	position	VERB
cana-5102	111	26	.	.	PUNCT
cana-5102	112	1	svm	svm	PROPN
cana-5102	112	2	,	,	PUNCT
cana-5102	112	3	or	or	CCONJ
cana-5102	112	4	support	support	VERB
cana-5102	112	5	vector	vector	NOUN
cana-5102	112	6	machines	machine	NOUN
cana-5102	112	7	,	,	PUNCT
cana-5102	112	8	are	be	AUX
cana-5102	112	9	used	use	VERB
cana-5102	112	10	for	for	ADP
cana-5102	112	11	the	the	DET
cana-5102	112	12	classification	classification	NOUN
cana-5102	112	13	phase	phase	NOUN
cana-5102	112	14	.	.	PUNCT
cana-5102	113	1	the	the	DET
cana-5102	113	2	experiment	experiment	NOUN
cana-5102	113	3	uses	use	VERB
cana-5102	113	4	data	datum	NOUN
cana-5102	113	5	from	from	ADP
cana-5102	113	6	mnist	mnist	NOUN
cana-5102	113	7	databases	database	NOUN
cana-5102	113	8	.	.	PUNCT
cana-5102	114	1	the	the	DET
cana-5102	114	2	communications	communication	NOUN
cana-5102	114	3	on	on	ADP
cana-5102	114	4	applied	apply	VERB
cana-5102	114	5	nonlinear	nonlinear	ADJ
cana-5102	114	6	analysis	analysis	NOUN
cana-5102	114	7	issn	issn	NOUN
cana-5102	114	8	:	:	PUNCT
cana-5102	114	9	1074	1074	NUM
cana-5102	114	10	-	-	PUNCT
cana-5102	114	11	133x	133x	NUM
cana-5102	114	12	vol	vol	NOUN
cana-5102	114	13	32	32	NUM
cana-5102	114	14	no	no	NOUN
cana-5102	114	15	.	.	PUNCT
cana-5102	115	1	icmasd	icmasd	NOUN
cana-5102	115	2	(	(	PUNCT
cana-5102	115	3	2025	2025	NUM
cana-5102	115	4	)	)	PUNCT
cana-5102	115	5	759	759	NUM
cana-5102	115	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-5102	115	7	svm	svm	ADJ
cana-5102	115	8	model	model	NOUN
cana-5102	115	9	is	be	AUX
cana-5102	115	10	established	establish	VERB
cana-5102	115	11	using	use	VERB
cana-5102	115	12	the	the	DET
cana-5102	115	13	radial	radial	ADJ
cana-5102	115	14	basis	basis	NOUN
cana-5102	115	15	function	function	NOUN
cana-5102	115	16	.	.	PUNCT
cana-5102	116	1	the	the	DET
cana-5102	116	2	links	link	NOUN
cana-5102	116	3	between	between	ADP
cana-5102	116	4	the	the	DET
cana-5102	116	5	parameters	parameter	NOUN
cana-5102	116	6	of	of	ADP
cana-5102	116	7	kernel	kernel	PROPN
cana-5102	116	8	are	be	AUX
cana-5102	116	9	finally	finally	ADV
cana-5102	116	10	discovered	discover	VERB
cana-5102	116	11	to	to	PART
cana-5102	116	12	improve	improve	VERB
cana-5102	116	13	training	training	NOUN
cana-5102	116	14	and	and	CCONJ
cana-5102	116	15	testing	testing	NOUN
cana-5102	116	16	accuracy	accuracy	NOUN
cana-5102	116	17	.	.	PUNCT
cana-5102	117	1	it	it	PRON
cana-5102	117	2	shows	show	VERB
cana-5102	117	3	86	86	NUM
cana-5102	117	4	%	%	NOUN
cana-5102	117	5	,	,	PUNCT
cana-5102	117	6	88	88	NUM
cana-5102	117	7	%	%	NOUN
cana-5102	117	8	,	,	PUNCT
cana-5102	117	9	73	73	NUM
cana-5102	117	10	%	%	NOUN
cana-5102	117	11	accuracy	accuracy	NOUN
cana-5102	117	12	for	for	ADP
cana-5102	117	13	first	first	ADJ
cana-5102	117	14	,	,	PUNCT
cana-5102	117	15	second	second	ADJ
cana-5102	117	16	and	and	CCONJ
cana-5102	117	17	third	third	ADJ
cana-5102	117	18	set	set	NOUN
cana-5102	117	19	of	of	ADP
cana-5102	117	20	datasets.[17	datasets.[17	PROPN
cana-5102	117	21	]	]	X
cana-5102	117	22	yadav	yadav	PROPN
cana-5102	117	23	et	et	PROPN
cana-5102	117	24	al	al	PROPN
cana-5102	117	25	.	.	PROPN
cana-5102	117	26	2013	2013	NUM
cana-5102	117	27	worked	work	VERB
cana-5102	117	28	on	on	ADP
cana-5102	117	29	hindi	hindi	NOUN
cana-5102	117	30	language	language	NOUN
cana-5102	117	31	as	as	SCONJ
cana-5102	117	32	it	it	PRON
cana-5102	117	33	is	be	AUX
cana-5102	117	34	very	very	ADV
cana-5102	117	35	difficult	difficult	ADJ
cana-5102	117	36	than	than	ADP
cana-5102	117	37	english	english	PROPN
cana-5102	117	38	as	as	SCONJ
cana-5102	117	39	it	it	PRON
cana-5102	117	40	does	do	AUX
cana-5102	117	41	not	not	PART
cana-5102	117	42	have	have	VERB
cana-5102	117	43	character	character	NOUN
cana-5102	117	44	separation	separation	NOUN
cana-5102	117	45	.	.	PUNCT
cana-5102	118	1	an	an	DET
cana-5102	118	2	ann	ann	PROPN
cana-5102	118	3	algorithm	algorithm	NOUN
cana-5102	118	4	is	be	AUX
cana-5102	118	5	used	use	VERB
cana-5102	118	6	for	for	ADP
cana-5102	118	7	hindi	hindi	NOUN
cana-5102	118	8	character	character	NOUN
cana-5102	118	9	recognition	recognition	NOUN
cana-5102	118	10	using	use	VERB
cana-5102	118	11	the	the	DET
cana-5102	118	12	grayscale	grayscale	NOUN
cana-5102	118	13	images	image	NOUN
cana-5102	118	14	processed	process	VERB
cana-5102	118	15	to	to	ADP
cana-5102	118	16	same	same	ADJ
cana-5102	118	17	level	level	NOUN
cana-5102	118	18	or	or	CCONJ
cana-5102	118	19	normalized	normalize	VERB
cana-5102	118	20	.	.	PUNCT
cana-5102	119	1	a	a	DET
cana-5102	119	2	back	back	ADJ
cana-5102	119	3	-	-	PUNCT
cana-5102	119	4	propagation	propagation	NOUN
cana-5102	119	5	neural	neural	ADJ
cana-5102	119	6	network	network	NOUN
cana-5102	119	7	with	with	ADP
cana-5102	119	8	two	two	NUM
cana-5102	119	9	hidden	hidden	ADJ
cana-5102	119	10	layers	layer	NOUN
cana-5102	119	11	is	be	AUX
cana-5102	119	12	utilized	utilize	VERB
cana-5102	119	13	to	to	PART
cana-5102	119	14	create	create	VERB
cana-5102	119	15	the	the	DET
cana-5102	119	16	neural	neural	ADJ
cana-5102	119	17	classifier	classifier	NOUN
cana-5102	119	18	.	.	PUNCT
cana-5102	120	1	in	in	ADP
cana-5102	120	2	order	order	NOUN
cana-5102	120	3	to	to	PART
cana-5102	120	4	build	build	VERB
cana-5102	120	5	the	the	DET
cana-5102	120	6	neural	neural	ADJ
cana-5102	120	7	classifiers	classifier	NOUN
cana-5102	120	8	,	,	PUNCT
cana-5102	120	9	a	a	DET
cana-5102	120	10	gradient	gradient	ADJ
cana-5102	120	11	decent	decent	ADJ
cana-5102	120	12	based	base	VERB
cana-5102	120	13	neural	neural	ADJ
cana-5102	120	14	network	network	NOUN
cana-5102	120	15	with	with	ADP
cana-5102	120	16	two	two	NUM
cana-5102	120	17	hidden	hidden	ADJ
cana-5102	120	18	layers	layer	NOUN
cana-5102	120	19	is	be	AUX
cana-5102	120	20	used	use	VERB
cana-5102	120	21	.	.	PUNCT
cana-5102	121	1	the	the	DET
cana-5102	121	2	model	model	NOUN
cana-5102	121	3	has	have	AUX
cana-5102	121	4	been	be	AUX
cana-5102	121	5	trained	train	VERB
cana-5102	121	6	and	and	CCONJ
cana-5102	121	7	evaluated	evaluate	VERB
cana-5102	121	8	on	on	ADP
cana-5102	121	9	hindi	hindi	NOUN
cana-5102	121	10	texts	text	NOUN
cana-5102	121	11	from	from	ADP
cana-5102	121	12	print	print	NOUN
cana-5102	121	13	.	.	PUNCT
cana-5102	122	1	a	a	DET
cana-5102	122	2	classification	classification	NOUN
cana-5102	122	3	results	result	NOUN
cana-5102	122	4	of	of	ADP
cana-5102	122	5	90	90	NUM
cana-5102	122	6	%	%	NOUN
cana-5102	122	7	is	be	AUX
cana-5102	122	8	possible	possible	ADJ
cana-5102	122	9	in	in	ADP
cana-5102	122	10	performing.[18	performing.[18	PROPN
cana-5102	122	11	]	]	PUNCT
cana-5102	122	12	gaur	gaur	NOUN
cana-5102	122	13	et	et	PROPN
cana-5102	122	14	al	al	PROPN
cana-5102	122	15	.	.	PROPN
cana-5102	122	16	2015	2015	NUM
cana-5102	122	17	a	a	DET
cana-5102	122	18	three	three	NUM
cana-5102	122	19	-	-	PUNCT
cana-5102	122	20	stage	stage	NOUN
cana-5102	122	21	process	process	NOUN
cana-5102	122	22	is	be	AUX
cana-5102	122	23	used	use	VERB
cana-5102	122	24	to	to	PART
cana-5102	122	25	recognize	recognize	VERB
cana-5102	122	26	hindi	hindi	NOUN
cana-5102	122	27	characters	character	NOUN
cana-5102	122	28	.	.	PUNCT
cana-5102	123	1	the	the	DET
cana-5102	123	2	classification	classification	NOUN
cana-5102	123	3	procedure	procedure	NOUN
cana-5102	123	4	is	be	AUX
cana-5102	123	5	the	the	DET
cana-5102	123	6	third	third	ADJ
cana-5102	123	7	stage	stage	NOUN
cana-5102	123	8	,	,	PUNCT
cana-5102	123	9	and	and	CCONJ
cana-5102	123	10	support	support	VERB
cana-5102	123	11	vector	vector	NOUN
cana-5102	123	12	machines	machine	NOUN
cana-5102	123	13	are	be	AUX
cana-5102	123	14	employed	employ	VERB
cana-5102	123	15	for	for	ADP
cana-5102	123	16	this	this	PRON
cana-5102	123	17	.	.	PUNCT
cana-5102	124	1	both	both	DET
cana-5102	124	2	euclidean	euclidean	ADJ
cana-5102	124	3	distance	distance	NOUN
cana-5102	124	4	and	and	CCONJ
cana-5102	124	5	a	a	DET
cana-5102	124	6	svm	svm	NOUN
cana-5102	124	7	are	be	AUX
cana-5102	124	8	used	use	VERB
cana-5102	124	9	to	to	PART
cana-5102	124	10	classify	classify	VERB
cana-5102	124	11	the	the	DET
cana-5102	124	12	results	result	NOUN
cana-5102	124	13	in	in	ADP
cana-5102	124	14	this	this	DET
cana-5102	124	15	calculation	calculation	NOUN
cana-5102	124	16	.	.	PUNCT
cana-5102	125	1	svm	svm	PROPN
cana-5102	125	2	produces	produce	VERB
cana-5102	125	3	better	well	ADJ
cana-5102	125	4	outcomes	outcome	NOUN
cana-5102	125	5	than	than	SCONJ
cana-5102	125	6	euclidean	euclidean	ADJ
cana-5102	125	7	distance	distance	NOUN
cana-5102	125	8	does	do	VERB
cana-5102	125	9	.	.	PUNCT
cana-5102	126	1	the	the	DET
cana-5102	126	2	highest	high	ADJ
cana-5102	126	3	outcome	outcome	NOUN
cana-5102	126	4	obtained	obtain	VERB
cana-5102	126	5	using	use	VERB
cana-5102	126	6	euclidean	euclidean	ADJ
cana-5102	126	7	distance	distance	NOUN
cana-5102	126	8	is	be	AUX
cana-5102	126	9	81.7	81.7	NUM
cana-5102	126	10	%	%	NOUN
cana-5102	126	11	.	.	PUNCT
cana-5102	127	1	svm	svm	PROPN
cana-5102	127	2	is	be	AUX
cana-5102	127	3	employed	employ	VERB
cana-5102	127	4	with	with	ADP
cana-5102	127	5	a	a	DET
cana-5102	127	6	linear	linear	ADJ
cana-5102	127	7	kernel	kernel	NOUN
cana-5102	127	8	,	,	PUNCT
cana-5102	127	9	and	and	CCONJ
cana-5102	127	10	the	the	DET
cana-5102	127	11	result	result	NOUN
cana-5102	127	12	is	be	AUX
cana-5102	127	13	95.86	95.86	NUM
cana-5102	127	14	%	%	NOUN
cana-5102	127	15	.	.	PUNCT
cana-5102	128	1	[	[	X
cana-5102	128	2	19	19	NUM
cana-5102	128	3	]	]	PUNCT
cana-5102	128	4	sonawane	sonawane	NOUN
cana-5102	128	5	et	et	PROPN
cana-5102	128	6	al	al	PROPN
cana-5102	128	7	.	.	PROPN
cana-5102	128	8	2015	2015	NUM
cana-5102	128	9	compared	compare	VERB
cana-5102	128	10	the	the	DET
cana-5102	128	11	accuracy	accuracy	NOUN
cana-5102	128	12	of	of	ADP
cana-5102	128	13	two	two	NUM
cana-5102	128	14	ocrs	ocr	NOUN
cana-5102	128	15	for	for	ADP
cana-5102	128	16	english	english	ADJ
cana-5102	128	17	language	language	NOUN
cana-5102	128	18	.	.	PUNCT
cana-5102	129	1	it	it	PRON
cana-5102	129	2	is	be	AUX
cana-5102	129	3	a	a	DET
cana-5102	129	4	component	component	NOUN
cana-5102	129	5	of	of	ADP
cana-5102	129	6	ann	ann	PROPN
cana-5102	129	7	and	and	CCONJ
cana-5102	129	8	the	the	DET
cana-5102	129	9	“	"	PUNCT
cana-5102	129	10	nearest	near	ADJ
cana-5102	129	11	neighbor	neighbor	NOUN
cana-5102	129	12	”	"	PUNCT
cana-5102	129	13	method	method	NOUN
cana-5102	129	14	are	be	AUX
cana-5102	129	15	the	the	DET
cana-5102	129	16	two	two	NUM
cana-5102	129	17	recognizers	recognizer	NOUN
cana-5102	129	18	that	that	PRON
cana-5102	129	19	are	be	AUX
cana-5102	129	20	employed	employ	VERB
cana-5102	129	21	.	.	PUNCT
cana-5102	130	1	binary	binary	ADJ
cana-5102	130	2	digits	digit	NOUN
cana-5102	130	3	must	must	AUX
cana-5102	130	4	be	be	AUX
cana-5102	130	5	used	use	VERB
cana-5102	130	6	to	to	PART
cana-5102	130	7	represent	represent	VERB
cana-5102	130	8	the	the	DET
cana-5102	130	9	characters	character	NOUN
cana-5102	130	10	.	.	PUNCT
cana-5102	131	1	to	to	PART
cana-5102	131	2	do	do	VERB
cana-5102	131	3	this	this	PRON
cana-5102	131	4	,	,	PUNCT
cana-5102	131	5	the	the	DET
cana-5102	131	6	symbols	symbol	NOUN
cana-5102	131	7	were	be	AUX
cana-5102	131	8	combined	combine	VERB
cana-5102	131	9	in	in	ADP
cana-5102	131	10	a	a	DET
cana-5102	131	11	matrix	matrix	NOUN
cana-5102	131	12	of	of	ADP
cana-5102	131	13	7	7	NUM
cana-5102	131	14	columns	column	NOUN
cana-5102	131	15	and	and	CCONJ
cana-5102	131	16	5	5	NUM
cana-5102	131	17	rows	row	NOUN
cana-5102	131	18	.	.	PUNCT
cana-5102	132	1	the	the	DET
cana-5102	132	2	implementers	implementer	NOUN
cana-5102	132	3	of	of	ADP
cana-5102	132	4	the	the	DET
cana-5102	132	5	recognizers	recognizer	NOUN
cana-5102	132	6	on	on	ADP
cana-5102	132	7	the	the	DET
cana-5102	132	8	extracted	extract	VERB
cana-5102	132	9	features	feature	NOUN
cana-5102	132	10	were	be	AUX
cana-5102	132	11	done	do	VERB
cana-5102	132	12	using	use	VERB
cana-5102	132	13	a	a	DET
cana-5102	132	14	math	math	NOUN
cana-5102	132	15	-	-	PUNCT
cana-5102	132	16	lab	lab	NOUN
cana-5102	132	17	program	program	NOUN
cana-5102	132	18	.	.	PUNCT
cana-5102	133	1	the	the	DET
cana-5102	133	2	outcome	outcome	NOUN
cana-5102	133	3	demonstrates	demonstrate	VERB
cana-5102	133	4	that	that	SCONJ
cana-5102	133	5	when	when	SCONJ
cana-5102	133	6	used	use	VERB
cana-5102	133	7	with	with	ADP
cana-5102	133	8	english	english	ADJ
cana-5102	133	9	characters	character	NOUN
cana-5102	133	10	,	,	PUNCT
cana-5102	133	11	nearest	near	ADJ
cana-5102	133	12	neighbor	neighbor	NOUN
cana-5102	133	13	is	be	AUX
cana-5102	133	14	a	a	DET
cana-5102	133	15	better	well	ADJ
cana-5102	133	16	character	character	NOUN
cana-5102	133	17	recognizer	recognizer	NOUN
cana-5102	133	18	.	.	PUNCT
cana-5102	134	1	accuracy	accuracy	NOUN
cana-5102	134	2	of	of	ADP
cana-5102	134	3	nn	nn	PROPN
cana-5102	134	4	is	be	AUX
cana-5102	134	5	nearly	nearly	ADV
cana-5102	134	6	57.69	57.69	NUM
cana-5102	134	7	%	%	NOUN
cana-5102	134	8	and	and	CCONJ
cana-5102	134	9	of	of	ADP
cana-5102	134	10	nearest	near	ADJ
cana-5102	134	11	neighbor	neighbor	NOUN
cana-5102	134	12	method	method	NOUN
cana-5102	134	13	as	as	ADP
cana-5102	134	14	61.53	61.53	NUM
cana-5102	134	15	%	%	NOUN
cana-5102	134	16	.	.	PUNCT
cana-5102	135	1	the	the	DET
cana-5102	135	2	outcome	outcome	NOUN
cana-5102	135	3	demonstrates	demonstrate	VERB
cana-5102	135	4	unequivocally	unequivocally	ADV
cana-5102	135	5	that	that	SCONJ
cana-5102	135	6	the	the	DET
cana-5102	135	7	“	"	PUNCT
cana-5102	135	8	nearest	near	ADJ
cana-5102	135	9	neighbor	neighbor	NOUN
cana-5102	135	10	”	"	PUNCT
cana-5102	135	11	approach	approach	NOUN
cana-5102	135	12	outperforms.[20	outperforms.[20	PROPN
cana-5102	135	13	]	]	PUNCT
cana-5102	135	14	singh	singh	PROPN
cana-5102	135	15	2018	2018	NUM
cana-5102	135	16	used	use	VERB
cana-5102	135	17	piecewise	piecewise	NOUN
cana-5102	135	18	histogram	histogram	NOUN
cana-5102	135	19	of	of	ADP
cana-5102	135	20	oriented	orient	VERB
cana-5102	135	21	gradient	gradient	NOUN
cana-5102	135	22	to	to	PART
cana-5102	135	23	collect	collect	VERB
cana-5102	135	24	information	information	NOUN
cana-5102	135	25	from	from	ADP
cana-5102	135	26	partitioned	partition	VERB
cana-5102	135	27	images	image	NOUN
cana-5102	135	28	to	to	PART
cana-5102	135	29	recognize	recognize	VERB
cana-5102	135	30	devanagari	devanagari	ADJ
cana-5102	135	31	language	language	NOUN
cana-5102	135	32	thus	thus	ADV
cana-5102	135	33	this	this	DET
cana-5102	135	34	paper	paper	NOUN
cana-5102	135	35	uses	use	VERB
cana-5102	135	36	a	a	DET
cana-5102	135	37	piecewise	piecewise	NOUN
cana-5102	135	38	feature	feature	NOUN
cana-5102	135	39	extraction	extraction	NOUN
cana-5102	135	40	method	method	NOUN
cana-5102	135	41	.	.	PUNCT
cana-5102	136	1	to	to	PART
cana-5102	136	2	classify	classify	VERB
cana-5102	136	3	a	a	DET
cana-5102	136	4	neural	neural	ADJ
cana-5102	136	5	network	network	NOUN
cana-5102	136	6	is	be	AUX
cana-5102	136	7	used	use	VERB
cana-5102	136	8	which	which	PRON
cana-5102	136	9	is	be	AUX
cana-5102	136	10	trained	train	VERB
cana-5102	136	11	with	with	ADP
cana-5102	136	12	partitioned	partition	VERB
cana-5102	136	13	hog	hog	NOUN
cana-5102	136	14	information	information	NOUN
cana-5102	136	15	.	.	PUNCT
cana-5102	137	1	it	it	PRON
cana-5102	137	2	acquires	acquire	VERB
cana-5102	137	3	the	the	DET
cana-5102	137	4	maximum	maximum	ADJ
cana-5102	137	5	accuracy	accuracy	NOUN
cana-5102	137	6	of	of	ADP
cana-5102	137	7	99.27	99.27	NUM
cana-5102	137	8	%	%	NOUN
cana-5102	137	9	and	and	CCONJ
cana-5102	137	10	average	average	NOUN
cana-5102	137	11	of	of	ADP
cana-5102	137	12	97.06	97.06	NUM
cana-5102	137	13	%	%	NOUN
cana-5102	137	14	of	of	ADP
cana-5102	137	15	accuracy.[21	accuracy.[21	PROPN
cana-5102	137	16	]	]	PUNCT
cana-5102	137	17	lamghari	lamghari	PROPN
cana-5102	137	18	et	et	PROPN
cana-5102	137	19	al	al	PROPN
cana-5102	137	20	.	.	PROPN
cana-5102	137	21	2018	2018	NUM
cana-5102	137	22	presented	present	VERB
cana-5102	137	23	an	an	DET
cana-5102	137	24	offline	offline	ADJ
cana-5102	137	25	character	character	NOUN
cana-5102	137	26	recognition	recognition	NOUN
cana-5102	137	27	process	process	NOUN
cana-5102	137	28	for	for	ADP
cana-5102	137	29	arabic	arabic	ADJ
cana-5102	137	30	handwritten	handwritten	ADJ
cana-5102	137	31	characters	character	NOUN
cana-5102	137	32	.	.	PUNCT
cana-5102	138	1	here	here	ADV
cana-5102	138	2	five	five	NUM
cana-5102	138	3	methods	method	NOUN
cana-5102	138	4	are	be	AUX
cana-5102	138	5	used	use	VERB
cana-5102	138	6	to	to	PART
cana-5102	138	7	extract	extract	VERB
cana-5102	138	8	66	66	NUM
cana-5102	138	9	structural	structural	ADJ
cana-5102	138	10	,	,	PUNCT
cana-5102	138	11	reginal	reginal	ADJ
cana-5102	138	12	and	and	CCONJ
cana-5102	138	13	statical	statical	ADJ
cana-5102	138	14	characteristics	characteristic	NOUN
cana-5102	138	15	which	which	PRON
cana-5102	138	16	are	be	AUX
cana-5102	138	17	treated	treat	VERB
cana-5102	138	18	in	in	ADP
cana-5102	138	19	a	a	DET
cana-5102	138	20	feed	feed	NOUN
cana-5102	138	21	forward	forward	ADV
cana-5102	138	22	neural	neural	ADJ
cana-5102	138	23	network	network	NOUN
cana-5102	138	24	with	with	ADP
cana-5102	138	25	hidden	hidden	ADJ
cana-5102	138	26	layers	layer	NOUN
cana-5102	138	27	.	.	PUNCT
cana-5102	139	1	database	database	NOUN
cana-5102	139	2	for	for	ADP
cana-5102	139	3	arabic	arabic	ADJ
cana-5102	139	4	handwritten	handwritten	ADJ
cana-5102	139	5	characters	character	NOUN
cana-5102	139	6	and	and	CCONJ
cana-5102	139	7	ligature	ligature	NOUN
cana-5102	139	8	(	(	PUNCT
cana-5102	139	9	dahcl	dahcl	PROPN
cana-5102	139	10	)	)	PUNCT
cana-5102	139	11	is	be	AUX
cana-5102	139	12	used	use	VERB
cana-5102	139	13	for	for	ADP
cana-5102	139	14	training	training	NOUN
cana-5102	139	15	and	and	CCONJ
cana-5102	139	16	testing	testing	NOUN
cana-5102	139	17	which	which	PRON
cana-5102	139	18	results	result	VERB
cana-5102	139	19	in	in	ADP
cana-5102	139	20	98.27	98.27	NUM
cana-5102	139	21	%	%	NOUN
cana-5102	139	22	accuracy	accuracy	NOUN
cana-5102	139	23	.	.	PUNCT
cana-5102	140	1	[	[	X
cana-5102	140	2	22	22	NUM
cana-5102	140	3	]	]	PUNCT
cana-5102	140	4	jebril	jebril	NOUN
cana-5102	140	5	proposes	propose	VERB
cana-5102	140	6	a	a	DET
cana-5102	140	7	character	character	NOUN
cana-5102	140	8	recognition	recognition	NOUN
cana-5102	140	9	system	system	NOUN
cana-5102	140	10	for	for	ADP
cana-5102	140	11	arabic	arabic	ADJ
cana-5102	140	12	language	language	NOUN
cana-5102	140	13	.	.	PUNCT
cana-5102	141	1	it	it	PRON
cana-5102	141	2	uses	use	VERB
cana-5102	141	3	hog	hog	NOUN
cana-5102	141	4	for	for	ADP
cana-5102	141	5	feature	feature	NOUN
cana-5102	141	6	abstraction	abstraction	NOUN
cana-5102	141	7	and	and	CCONJ
cana-5102	141	8	svm	svm	ADJ
cana-5102	141	9	for	for	ADP
cana-5102	141	10	classification	classification	NOUN
cana-5102	141	11	.	.	PUNCT
cana-5102	142	1	paper	paper	NOUN
cana-5102	142	2	uses	use	VERB
cana-5102	142	3	its	its	PRON
cana-5102	142	4	own	own	ADJ
cana-5102	142	5	dataset	dataset	NOUN
cana-5102	142	6	of	of	ADP
cana-5102	142	7	43000	43000	NUM
cana-5102	142	8	where	where	SCONJ
cana-5102	142	9	30000	30000	NUM
cana-5102	142	10	is	be	AUX
cana-5102	142	11	used	use	VERB
cana-5102	142	12	for	for	ADP
cana-5102	142	13	training	training	NOUN
cana-5102	142	14	purposes	purpose	NOUN
cana-5102	142	15	and	and	CCONJ
cana-5102	142	16	13000	13000	NUM
cana-5102	142	17	for	for	ADP
cana-5102	142	18	testing	testing	NOUN
cana-5102	142	19	purposes	purpose	NOUN
cana-5102	142	20	.	.	PUNCT
cana-5102	143	1	the	the	DET
cana-5102	143	2	result	result	NOUN
cana-5102	143	3	tends	tend	VERB
cana-5102	143	4	to	to	PART
cana-5102	143	5	be	be	AUX
cana-5102	143	6	successful	successful	ADJ
cana-5102	143	7	as	as	SCONJ
cana-5102	143	8	it	it	PRON
cana-5102	143	9	provides	provide	VERB
cana-5102	143	10	an	an	DET
cana-5102	143	11	accuracy	accuracy	NOUN
cana-5102	143	12	of	of	ADP
cana-5102	143	13	above	above	ADP
cana-5102	143	14	99%.[23	99%.[23	NUM
cana-5102	143	15	]	]	X
cana-5102	143	16	koellner	koellner	NOUN
cana-5102	143	17	et	et	PROPN
cana-5102	143	18	al	al	PROPN
cana-5102	143	19	.	.	PUNCT
cana-5102	144	1	[	[	X
cana-5102	144	2	24	24	NUM
cana-5102	144	3	]	]	SYM
cana-5102	144	4	2019	2019	NUM
cana-5102	144	5	proposed	propose	VERB
cana-5102	144	6	a	a	DET
cana-5102	144	7	methodology	methodology	NOUN
cana-5102	144	8	for	for	ADP
cana-5102	144	9	online	online	ADJ
cana-5102	144	10	character	character	NOUN
cana-5102	144	11	recognition	recognition	NOUN
cana-5102	144	12	.	.	PUNCT
cana-5102	145	1	here	here	ADV
cana-5102	145	2	input	input	NOUN
cana-5102	145	3	is	be	AUX
cana-5102	145	4	taken	take	VERB
cana-5102	145	5	by	by	ADP
cana-5102	145	6	gyro	gyro	NOUN
cana-5102	145	7	meter	meter	NOUN
cana-5102	145	8	and	and	CCONJ
cana-5102	145	9	the	the	DET
cana-5102	145	10	time	time	NOUN
cana-5102	145	11	instance	instance	NOUN
cana-5102	145	12	,	,	PUNCT
cana-5102	145	13	acceleration	acceleration	NOUN
cana-5102	145	14	in	in	ADP
cana-5102	145	15	different	different	ADJ
cana-5102	145	16	directions	direction	NOUN
cana-5102	145	17	which	which	PRON
cana-5102	145	18	then	then	ADV
cana-5102	145	19	is	be	AUX
cana-5102	145	20	changed	change	VERB
cana-5102	145	21	into	into	ADP
cana-5102	145	22	information	information	NOUN
cana-5102	145	23	through	through	ADP
cana-5102	145	24	fast	fast	ADJ
cana-5102	145	25	fourier	fourier	NOUN
cana-5102	145	26	transform	transform	NOUN
cana-5102	145	27	,	,	PUNCT
cana-5102	145	28	wavelet	wavelet	NOUN
cana-5102	145	29	and	and	CCONJ
cana-5102	145	30	autocorrelation	autocorrelation	NOUN
cana-5102	145	31	functions	function	NOUN
cana-5102	145	32	.	.	PUNCT
cana-5102	146	1	this	this	DET
cana-5102	146	2	information	information	NOUN
cana-5102	146	3	is	be	AUX
cana-5102	146	4	used	use	VERB
cana-5102	146	5	with	with	ADP
cana-5102	146	6	knn	knn	PROPN
cana-5102	146	7	,	,	PUNCT
cana-5102	146	8	linear	linear	ADJ
cana-5102	146	9	discriminant	discriminant	ADJ
cana-5102	146	10	analysis	analysis	NOUN
cana-5102	146	11	and	and	CCONJ
cana-5102	146	12	naïve	naïve	ADJ
cana-5102	146	13	bayes	bayes	NOUN
cana-5102	146	14	as	as	ADP
cana-5102	146	15	classification	classification	NOUN
cana-5102	146	16	phase	phase	NOUN
cana-5102	146	17	.	.	PUNCT
cana-5102	147	1	nearly	nearly	ADV
cana-5102	147	2	a	a	DET
cana-5102	147	3	dataset	dataset	NOUN
cana-5102	147	4	of	of	ADP
cana-5102	147	5	20,000	20,000	NUM
cana-5102	147	6	lower	low	ADJ
cana-5102	147	7	case	case	NOUN
cana-5102	147	8	alphabet	alphabet	NOUN
cana-5102	147	9	is	be	AUX
cana-5102	147	10	used	use	VERB
cana-5102	147	11	provided	provide	VERB
cana-5102	147	12	by	by	ADP
cana-5102	147	13	15	15	NUM
cana-5102	147	14	people	people	NOUN
cana-5102	147	15	.	.	PUNCT
cana-5102	148	1	the	the	DET
cana-5102	148	2	result	result	NOUN
cana-5102	148	3	comes	come	VERB
cana-5102	148	4	out	out	ADP
cana-5102	148	5	to	to	PART
cana-5102	148	6	be	be	AUX
cana-5102	148	7	different	different	ADJ
cana-5102	148	8	depending	depend	VERB
cana-5102	148	9	on	on	ADP
cana-5102	148	10	if	if	SCONJ
cana-5102	148	11	dataset	dataset	VERB
cana-5102	148	12	used	use	VERB
cana-5102	148	13	for	for	ADP
cana-5102	148	14	training	training	NOUN
cana-5102	148	15	is	be	AUX
cana-5102	148	16	of	of	ADP
cana-5102	148	17	the	the	DET
cana-5102	148	18	user	user	NOUN
cana-5102	148	19	or	or	CCONJ
cana-5102	148	20	not	not	PART
cana-5102	148	21	.	.	PUNCT
cana-5102	149	1	for	for	ADP
cana-5102	149	2	user	user	NOUN
cana-5102	149	3	dependent	dependent	ADJ
cana-5102	149	4	dataset	dataset	NOUN
cana-5102	149	5	and	and	CCONJ
cana-5102	149	6	training	training	NOUN
cana-5102	149	7	accuracy	accuracy	NOUN
cana-5102	149	8	is	be	AUX
cana-5102	149	9	95	95	NUM
cana-5102	149	10	%	%	NOUN
cana-5102	149	11	,	,	PUNCT
cana-5102	149	12	92	92	NUM
cana-5102	149	13	%	%	NOUN
cana-5102	149	14	and	and	CCONJ
cana-5102	149	15	84	84	NUM
cana-5102	149	16	%	%	NOUN
cana-5102	149	17	while	while	NOUN
cana-5102	149	18	for	for	SCONJ
cana-5102	149	19	general	general	ADJ
cana-5102	149	20	dataset	dataset	NOUN
cana-5102	149	21	it	it	PRON
cana-5102	149	22	is	be	AUX
cana-5102	149	23	36	36	NUM
cana-5102	149	24	%	%	NOUN
cana-5102	149	25	,	,	PUNCT
cana-5102	149	26	46	46	NUM
cana-5102	149	27	%	%	NOUN
cana-5102	149	28	and	and	CCONJ
cana-5102	149	29	52	52	NUM
cana-5102	149	30	%	%	NOUN
cana-5102	149	31	for	for	ADP
cana-5102	149	32	knn	knn	PROPN
cana-5102	149	33	,	,	PUNCT
cana-5102	149	34	lda	lda	PROPN
cana-5102	149	35	and	and	CCONJ
cana-5102	149	36	lstm	lstm	PROPN
cana-5102	149	37	respectively	respectively	ADV
cana-5102	149	38	.	.	PUNCT
cana-5102	150	1	gurbuz	gurbuz	NOUN
cana-5102	150	2	et	et	PROPN
cana-5102	150	3	al	al	PROPN
cana-5102	150	4	.	.	PROPN
cana-5102	150	5	2020	2020	NUM
cana-5102	150	6	used	use	VERB
cana-5102	150	7	rff	rff	NOUN
cana-5102	150	8	sensor	sensor	NOUN
cana-5102	150	9	for	for	ADP
cana-5102	150	10	sign	sign	NOUN
cana-5102	150	11	language	language	NOUN
cana-5102	150	12	where	where	SCONJ
cana-5102	150	13	it	it	PRON
cana-5102	150	14	uses	use	VERB
cana-5102	150	15	simple	simple	ADJ
cana-5102	150	16	machine	machine	NOUN
cana-5102	150	17	learning	learn	VERB
cana-5102	150	18	technology	technology	NOUN
cana-5102	150	19	to	to	PART
cana-5102	150	20	achieve	achieve	VERB
cana-5102	150	21	accuracy	accuracy	NOUN
cana-5102	150	22	of	of	ADP
cana-5102	150	23	72.5%.[25	72.5%.[25	NUM
cana-5102	150	24	]	]	PUNCT
cana-5102	150	25	communications	communication	NOUN
cana-5102	150	26	on	on	ADP
cana-5102	150	27	applied	apply	VERB
cana-5102	150	28	nonlinear	nonlinear	ADJ
cana-5102	150	29	analysis	analysis	NOUN
cana-5102	150	30	issn	issn	NOUN
cana-5102	150	31	:	:	PUNCT
cana-5102	150	32	1074	1074	NUM
cana-5102	150	33	-	-	PUNCT
cana-5102	150	34	133x	133x	NUM
cana-5102	150	35	vol	vol	NOUN
cana-5102	150	36	32	32	NUM
cana-5102	150	37	no	no	NOUN
cana-5102	150	38	.	.	PUNCT
cana-5102	151	1	icmasd	icmasd	NOUN
cana-5102	151	2	(	(	PUNCT
cana-5102	151	3	2025	2025	NUM
cana-5102	151	4	)	)	PUNCT
cana-5102	152	1	760	760	NUM
cana-5102	152	2	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	152	3	liu	liu	PROPN
cana-5102	152	4	et	et	PROPN
cana-5102	152	5	al	al	PROPN
cana-5102	152	6	.	.	PROPN
cana-5102	152	7	2020	2020	NUM
cana-5102	152	8	proposed	propose	VERB
cana-5102	152	9	a	a	DET
cana-5102	152	10	novel	novel	ADJ
cana-5102	152	11	idea	idea	NOUN
cana-5102	152	12	of	of	ADP
cana-5102	152	13	a	a	DET
cana-5102	152	14	model	model	NOUN
cana-5102	152	15	called	call	VERB
cana-5102	152	16	stroke	stroke	NOUN
cana-5102	152	17	sequence	sequence	NOUN
cana-5102	152	18	-	-	PUNCT
cana-5102	152	19	dependent	dependent	ADJ
cana-5102	152	20	deep	deep	ADJ
cana-5102	152	21	convolutional	convolutional	ADJ
cana-5102	152	22	neural	neural	ADJ
cana-5102	152	23	network	network	NOUN
cana-5102	152	24	(	(	PUNCT
cana-5102	152	25	ssdcnn	ssdcnn	PROPN
cana-5102	152	26	)	)	PUNCT
cana-5102	152	27	which	which	PRON
cana-5102	152	28	uses	use	VERB
cana-5102	152	29	the	the	DET
cana-5102	152	30	eight	eight	NUM
cana-5102	152	31	directional	directional	ADJ
cana-5102	152	32	method	method	NOUN
cana-5102	152	33	and	and	CCONJ
cana-5102	152	34	stroke	stroke	NOUN
cana-5102	152	35	sequence	sequence	NOUN
cana-5102	152	36	to	to	PART
cana-5102	152	37	create	create	VERB
cana-5102	152	38	a	a	DET
cana-5102	152	39	chinese	chinese	ADJ
cana-5102	152	40	character	character	NOUN
cana-5102	152	41	recognition	recognition	NOUN
cana-5102	152	42	system	system	NOUN
cana-5102	152	43	.	.	PUNCT
cana-5102	153	1	the	the	DET
cana-5102	153	2	process	process	NOUN
cana-5102	153	3	is	be	AUX
cana-5102	153	4	mainly	mainly	ADV
cana-5102	153	5	divided	divide	VERB
cana-5102	153	6	into	into	ADP
cana-5102	153	7	two	two	NUM
cana-5102	153	8	parts	part	NOUN
cana-5102	153	9	1	1	NUM
cana-5102	153	10	.	.	PUNCT
cana-5102	153	11	creating	create	VERB
cana-5102	153	12	the	the	DET
cana-5102	153	13	stack	stack	NOUN
cana-5102	153	14	of	of	ADP
cana-5102	153	15	strokes	stroke	NOUN
cana-5102	153	16	in	in	ADP
cana-5102	153	17	the	the	DET
cana-5102	153	18	writing	writing	NOUN
cana-5102	153	19	order	order	NOUN
cana-5102	153	20	of	of	ADP
cana-5102	153	21	them	they	PRON
cana-5102	153	22	2	2	NUM
cana-5102	153	23	.	.	PUNCT
cana-5102	153	24	representation	representation	NOUN
cana-5102	153	25	of	of	ADP
cana-5102	153	26	character	character	NOUN
cana-5102	153	27	in	in	ADP
cana-5102	153	28	terms	term	NOUN
cana-5102	153	29	of	of	ADP
cana-5102	153	30	directional	directional	ADJ
cana-5102	153	31	features	feature	NOUN
cana-5102	153	32	.	.	PUNCT
cana-5102	154	1	after	after	ADP
cana-5102	154	2	completing	complete	VERB
cana-5102	154	3	the	the	DET
cana-5102	154	4	above	above	ADJ
cana-5102	154	5	two	two	NUM
cana-5102	154	6	steps	step	NOUN
cana-5102	154	7	both	both	DET
cana-5102	154	8	features	feature	NOUN
cana-5102	154	9	are	be	AUX
cana-5102	154	10	combined	combine	VERB
cana-5102	154	11	with	with	ADP
cana-5102	154	12	each	each	DET
cana-5102	154	13	other	other	ADJ
cana-5102	154	14	,	,	PUNCT
cana-5102	154	15	and	and	CCONJ
cana-5102	154	16	neural	neural	ADJ
cana-5102	154	17	network	network	NOUN
cana-5102	154	18	is	be	AUX
cana-5102	154	19	trained	train	VERB
cana-5102	154	20	.	.	PUNCT
cana-5102	155	1	this	this	DET
cana-5102	155	2	model	model	NOUN
cana-5102	155	3	has	have	VERB
cana-5102	155	4	high	high	ADJ
cana-5102	155	5	accuracy	accuracy	NOUN
cana-5102	155	6	of	of	ADP
cana-5102	155	7	97.86	97.86	NUM
cana-5102	155	8	%	%	NOUN
cana-5102	155	9	while	while	SCONJ
cana-5102	155	10	the	the	DET
cana-5102	155	11	error	error	NOUN
cana-5102	155	12	rate	rate	NOUN
cana-5102	155	13	is	be	AUX
cana-5102	155	14	58.28	58.28	NUM
cana-5102	155	15	%	%	NOUN
cana-5102	155	16	lower	low	ADJ
cana-5102	155	17	than	than	SCONJ
cana-5102	155	18	the	the	DET
cana-5102	155	19	eight	eight	NUM
cana-5102	155	20	directions	direction	NOUN
cana-5102	155	21	feature	feature	VERB
cana-5102	155	22	alone	alone	ADV
cana-5102	155	23	model.[26	model.[26	PROPN
cana-5102	155	24	]	]	X
cana-5102	155	25	saez	saez	PROPN
cana-5102	155	26	-	-	PUNCT
cana-5102	155	27	mingorance	mingorance	PROPN
cana-5102	155	28	et	et	PROPN
cana-5102	155	29	al	al	PROPN
cana-5102	155	30	.	.	PROPN
cana-5102	155	31	2021	2021	NUM
cana-5102	156	1	[	[	X
cana-5102	156	2	27	27	NUM
cana-5102	156	3	]	]	PUNCT
cana-5102	156	4	uses	use	VERB
cana-5102	156	5	ultrasonic	ultrasonic	ADJ
cana-5102	156	6	transceiver	transceiver	NOUN
cana-5102	156	7	with	with	ADP
cana-5102	156	8	cnn	cnn	PROPN
cana-5102	156	9	,	,	PUNCT
cana-5102	156	10	lstm	lstm	NOUN
cana-5102	156	11	,	,	PUNCT
cana-5102	156	12	convolutional	convolutional	ADJ
cana-5102	156	13	autoencoder	autoencoder	NOUN
cana-5102	156	14	for	for	ADP
cana-5102	156	15	character	character	NOUN
cana-5102	156	16	recognition	recognition	NOUN
cana-5102	156	17	.	.	PUNCT
cana-5102	157	1	as	as	SCONJ
cana-5102	157	2	result	result	VERB
cana-5102	157	3	it	it	PRON
cana-5102	157	4	gets	get	VERB
cana-5102	157	5	99.51	99.51	NUM
cana-5102	157	6	%	%	NOUN
cana-5102	157	7	accuracy	accuracy	NOUN
cana-5102	157	8	with	with	ADP
cana-5102	157	9	71	71	NUM
cana-5102	157	10	ms	ms	NOUN
cana-5102	157	11	latency	latency	NOUN
cana-5102	157	12	.	.	PUNCT
cana-5102	158	1	babiker	babiker	PROPN
cana-5102	158	2	hamdan	hamdan	PROPN
cana-5102	158	3	2021	2021	NUM
cana-5102	158	4	compares	compare	VERB
cana-5102	158	5	different	different	ADJ
cana-5102	158	6	feature	feature	NOUN
cana-5102	158	7	extraction	extraction	NOUN
cana-5102	158	8	methods	method	NOUN
cana-5102	158	9	and	and	CCONJ
cana-5102	158	10	classification	classification	NOUN
cana-5102	158	11	methods	method	NOUN
cana-5102	158	12	to	to	PART
cana-5102	158	13	recognize	recognize	VERB
cana-5102	158	14	english	english	ADJ
cana-5102	158	15	character	character	NOUN
cana-5102	158	16	and	and	CCONJ
cana-5102	158	17	numerals	numeral	NOUN
cana-5102	158	18	.	.	PUNCT
cana-5102	159	1	this	this	DET
cana-5102	159	2	paper	paper	NOUN
cana-5102	159	3	tries	try	VERB
cana-5102	159	4	to	to	PART
cana-5102	159	5	improve	improve	VERB
cana-5102	159	6	accuracy	accuracy	NOUN
cana-5102	159	7	with	with	ADP
cana-5102	159	8	the	the	DET
cana-5102	159	9	help	help	NOUN
cana-5102	159	10	of	of	ADP
cana-5102	159	11	combination	combination	NOUN
cana-5102	159	12	of	of	ADP
cana-5102	159	13	different	different	ADJ
cana-5102	159	14	techniques	technique	NOUN
cana-5102	159	15	.	.	PUNCT
cana-5102	160	1	statical	statical	ADJ
cana-5102	160	2	method	method	NOUN
cana-5102	160	3	,	,	PUNCT
cana-5102	160	4	template	template	NOUN
cana-5102	160	5	matching	matching	NOUN
cana-5102	160	6	method	method	NOUN
cana-5102	160	7	,	,	PUNCT
cana-5102	160	8	structural	structural	ADJ
cana-5102	160	9	pattern	pattern	NOUN
cana-5102	160	10	acknowledgement	acknowledgement	NOUN
cana-5102	160	11	and	and	CCONJ
cana-5102	160	12	statical	statical	ADJ
cana-5102	160	13	svm	svm	PROPN
cana-5102	160	14	are	be	AUX
cana-5102	160	15	used	use	VERB
cana-5102	160	16	which	which	PRON
cana-5102	160	17	give	give	VERB
cana-5102	160	18	accuracy	accuracy	NOUN
cana-5102	160	19	of	of	ADP
cana-5102	160	20	86	86	NUM
cana-5102	160	21	,	,	PUNCT
cana-5102	160	22	65	65	NUM
cana-5102	160	23	,	,	PUNCT
cana-5102	160	24	75	75	NUM
cana-5102	160	25	,	,	PUNCT
cana-5102	160	26	88	88	NUM
cana-5102	160	27	percent	percent	NOUN
cana-5102	160	28	and	and	CCONJ
cana-5102	160	29	sensitivity	sensitivity	NOUN
cana-5102	160	30	is	be	AUX
cana-5102	160	31	82	82	NUM
cana-5102	160	32	,	,	PUNCT
cana-5102	160	33	70	70	NUM
cana-5102	160	34	,	,	PUNCT
cana-5102	160	35	71	71	NUM
cana-5102	160	36	,	,	PUNCT
cana-5102	160	37	89	89	NUM
cana-5102	160	38	percentage	percentage	NOUN
cana-5102	160	39	respectively.[28	respectively.[28	NOUN
cana-5102	160	40	]	]	PUNCT
cana-5102	160	41	alemayoh	alemayoh	NOUN
cana-5102	160	42	et	et	PROPN
cana-5102	160	43	al	al	PROPN
cana-5102	160	44	.	.	PROPN
cana-5102	160	45	2022	2022	NUM
cana-5102	161	1	[	[	X
cana-5102	161	2	29	29	NUM
cana-5102	161	3	]	]	PUNCT
cana-5102	161	4	develops	develop	VERB
cana-5102	161	5	a	a	DET
cana-5102	161	6	deep	deep	ADJ
cana-5102	161	7	learning	learn	VERB
cana-5102	161	8	smart	smart	ADJ
cana-5102	161	9	compact	compact	ADJ
cana-5102	161	10	pen	pen	NOUN
cana-5102	161	11	that	that	PRON
cana-5102	161	12	can	can	AUX
cana-5102	161	13	recognize	recognize	VERB
cana-5102	161	14	36	36	NUM
cana-5102	161	15	letters	letter	NOUN
cana-5102	161	16	.	.	PUNCT
cana-5102	162	1	it	it	PRON
cana-5102	162	2	uses	use	VERB
cana-5102	162	3	dnn	dnn	PROPN
cana-5102	162	4	,	,	PUNCT
cana-5102	162	5	lstm	lstm	PROPN
cana-5102	162	6	,	,	PUNCT
cana-5102	162	7	cnn	cnn	PROPN
cana-5102	162	8	to	to	PART
cana-5102	162	9	create	create	VERB
cana-5102	162	10	a	a	DET
cana-5102	162	11	network	network	NOUN
cana-5102	162	12	that	that	PRON
cana-5102	162	13	has	have	VERB
cana-5102	162	14	validation	validation	NOUN
cana-5102	162	15	accuracy	accuracy	NOUN
cana-5102	162	16	of	of	ADP
cana-5102	162	17	99.05	99.05	NUM
cana-5102	162	18	%	%	NOUN
cana-5102	162	19	.	.	PUNCT
cana-5102	163	1	zhang	zhang	PROPN
cana-5102	163	2	et	et	PROPN
cana-5102	163	3	al	al	PROPN
cana-5102	163	4	.	.	PROPN
cana-5102	163	5	2022	2022	NUM
cana-5102	163	6	proposes	propose	VERB
cana-5102	163	7	a	a	DET
cana-5102	163	8	device	device	NOUN
cana-5102	163	9	to	to	PART
cana-5102	163	10	recognize	recognize	VERB
cana-5102	163	11	the	the	DET
cana-5102	163	12	letter	letter	NOUN
cana-5102	163	13	written	write	VERB
cana-5102	163	14	by	by	ADP
cana-5102	163	15	finger	finger	NOUN
cana-5102	163	16	when	when	SCONJ
cana-5102	163	17	worn	wear	VERB
cana-5102	163	18	by	by	ADP
cana-5102	163	19	it	it	PRON
cana-5102	163	20	.	.	PUNCT
cana-5102	164	1	this	this	DET
cana-5102	164	2	device	device	NOUN
cana-5102	164	3	uses	use	VERB
cana-5102	164	4	deep	deep	ADJ
cana-5102	164	5	learning	learning	NOUN
cana-5102	164	6	like	like	ADP
cana-5102	164	7	cnn	cnn	PROPN
cana-5102	164	8	to	to	PART
cana-5102	164	9	recognize	recognize	VERB
cana-5102	164	10	the	the	DET
cana-5102	164	11	36	36	NUM
cana-5102	164	12	characters	character	NOUN
cana-5102	164	13	(	(	PUNCT
cana-5102	164	14	26	26	NUM
cana-5102	164	15	english	english	NOUN
cana-5102	164	16	+	+	CCONJ
cana-5102	164	17	10	10	NUM
cana-5102	164	18	numerals	numeral	NOUN
cana-5102	164	19	)	)	PUNCT
cana-5102	164	20	which	which	PRON
cana-5102	164	21	results	result	VERB
cana-5102	164	22	in	in	ADP
cana-5102	164	23	97.95	97.95	NUM
cana-5102	164	24	%	%	NOUN
cana-5102	164	25	accuracy.[30	accuracy.[30	NOUN
cana-5102	164	26	]	]	X
cana-5102	164	27	bhattacharyya	bhattacharyya	PROPN
cana-5102	164	28	et	et	PROPN
cana-5102	164	29	al	al	PROPN
cana-5102	164	30	.	.	PROPN
cana-5102	164	31	2022	2022	NUM
cana-5102	164	32	proposed	propose	VERB
cana-5102	164	33	a	a	DET
cana-5102	164	34	two	two	NUM
cana-5102	164	35	-	-	PUNCT
cana-5102	164	36	stage	stage	NOUN
cana-5102	164	37	deep	deep	ADJ
cana-5102	164	38	feature	feature	NOUN
cana-5102	164	39	selection	selection	NOUN
cana-5102	164	40	approach	approach	NOUN
cana-5102	164	41	for	for	ADP
cana-5102	164	42	recognition	recognition	NOUN
cana-5102	164	43	of	of	ADP
cana-5102	164	44	devanagari	devanagari	NOUN
cana-5102	164	45	and	and	CCONJ
cana-5102	164	46	bangla	bangla	PROPN
cana-5102	164	47	language	language	NOUN
cana-5102	164	48	.	.	PUNCT
cana-5102	165	1	first	first	ADV
cana-5102	165	2	the	the	DET
cana-5102	165	3	features	feature	NOUN
cana-5102	165	4	are	be	AUX
cana-5102	165	5	ranked	rank	VERB
cana-5102	165	6	by	by	ADP
cana-5102	165	7	filter	filter	NOUN
cana-5102	165	8	method	method	NOUN
cana-5102	165	9	relief	relief	NOUN
cana-5102	165	10	and	and	CCONJ
cana-5102	165	11	then	then	ADV
cana-5102	165	12	the	the	DET
cana-5102	165	13	ranked	rank	VERB
cana-5102	165	14	feature	feature	NOUN
cana-5102	165	15	are	be	AUX
cana-5102	165	16	optimized	optimize	VERB
cana-5102	165	17	by	by	ADP
cana-5102	165	18	gray	gray	ADJ
cana-5102	165	19	wolf	wolf	PROPN
cana-5102	165	20	.	.	PUNCT
cana-5102	166	1	as	as	SCONJ
cana-5102	166	2	result	result	NOUN
cana-5102	166	3	accuracy	accuracy	NOUN
cana-5102	166	4	reaches	reach	VERB
cana-5102	166	5	100	100	NUM
cana-5102	166	6	%	%	NOUN
cana-5102	166	7	for	for	ADP
cana-5102	166	8	bangla	bangla	PROPN
cana-5102	166	9	and	and	CCONJ
cana-5102	166	10	99.61	99.61	NUM
cana-5102	166	11	%	%	NOUN
cana-5102	166	12	for	for	ADP
cana-5102	166	13	devanagari	devanagari	NOUN
cana-5102	166	14	.	.	PUNCT
cana-5102	167	1	[	[	X
cana-5102	167	2	31	31	NUM
cana-5102	167	3	]	]	X
cana-5102	167	4	zhang	zhang	PROPN
cana-5102	167	5	et	et	PROPN
cana-5102	167	6	al	al	PROPN
cana-5102	167	7	.	.	PROPN
cana-5102	167	8	2023	2023	NUM
cana-5102	167	9	proposes	propose	VERB
cana-5102	167	10	a	a	DET
cana-5102	167	11	finger	finger	NOUN
cana-5102	167	12	writing	write	VERB
cana-5102	167	13	recognition	recognition	NOUN
cana-5102	167	14	using	use	VERB
cana-5102	167	15	time	time	NOUN
cana-5102	167	16	of	of	ADP
cana-5102	167	17	flight	flight	NOUN
cana-5102	167	18	distance	distance	NOUN
cana-5102	167	19	tool	tool	NOUN
cana-5102	167	20	to	to	PART
cana-5102	167	21	take	take	VERB
cana-5102	167	22	input	input	NOUN
cana-5102	167	23	.	.	PUNCT
cana-5102	168	1	the	the	DET
cana-5102	168	2	database	database	NOUN
cana-5102	168	3	of	of	ADP
cana-5102	168	4	21	21	NUM
cana-5102	168	5	users	user	NOUN
cana-5102	168	6	is	be	AUX
cana-5102	168	7	taken	take	VERB
cana-5102	168	8	for	for	ADP
cana-5102	168	9	english	english	ADJ
cana-5102	168	10	language	language	NOUN
cana-5102	168	11	.	.	PUNCT
cana-5102	169	1	deep	deep	ADJ
cana-5102	169	2	learning	learning	NOUN
cana-5102	169	3	algorithms	algorithm	NOUN
cana-5102	169	4	such	such	ADJ
cana-5102	169	5	as	as	ADP
cana-5102	169	6	lstm	lstm	PROPN
cana-5102	169	7	,	,	PUNCT
cana-5102	169	8	cnn	cnn	PROPN
cana-5102	169	9	are	be	AUX
cana-5102	169	10	used	use	VERB
cana-5102	169	11	for	for	ADP
cana-5102	169	12	classification	classification	NOUN
cana-5102	169	13	where	where	SCONJ
cana-5102	169	14	lstm	lstm	NOUN
cana-5102	169	15	gives	give	VERB
cana-5102	169	16	best	good	ADJ
cana-5102	169	17	result	result	NOUN
cana-5102	169	18	with	with	ADP
cana-5102	169	19	98.31	98.31	NUM
cana-5102	169	20	%	%	NOUN
cana-5102	169	21	accuracy.[32	accuracy.[32	PROPN
cana-5102	169	22	]	]	PUNCT
cana-5102	169	23	bwatiramba	bwatiramba	VERB
cana-5102	169	24	et	et	PROPN
cana-5102	169	25	al	al	PROPN
cana-5102	169	26	.	.	PROPN
cana-5102	169	27	2023	2023	NUM
cana-5102	169	28	uses	use	VERB
cana-5102	169	29	cnn	cnn	PROPN
cana-5102	169	30	and	and	CCONJ
cana-5102	169	31	snn	snn	PROPN
cana-5102	169	32	for	for	ADP
cana-5102	169	33	numerals	numeral	NOUN
cana-5102	169	34	character	character	NOUN
cana-5102	169	35	recognition	recognition	NOUN
cana-5102	169	36	.	.	PUNCT
cana-5102	170	1	it	it	PRON
cana-5102	170	2	achieves	achieve	VERB
cana-5102	170	3	98.13	98.13	NUM
cana-5102	170	4	%	%	NOUN
cana-5102	170	5	accuracy	accuracy	NOUN
cana-5102	170	6	.	.	PUNCT
cana-5102	171	1	[	[	X
cana-5102	171	2	33]bhatti	33]bhatti	NUM
cana-5102	171	3	et	et	NOUN
cana-5102	171	4	al	al	PROPN
cana-5102	171	5	.	.	PROPN
cana-5102	171	6	2023	2023	NUM
cana-5102	171	7	proposes	propose	VERB
cana-5102	171	8	to	to	PART
cana-5102	171	9	recognize	recognize	VERB
cana-5102	171	10	urdu	urdu	PROPN
cana-5102	171	11	language	language	NOUN
cana-5102	171	12	using	use	VERB
cana-5102	171	13	cnn	cnn	PROPN
cana-5102	171	14	for	for	ADP
cana-5102	171	15	feature	feature	NOUN
cana-5102	171	16	extraction	extraction	NOUN
cana-5102	171	17	and	and	CCONJ
cana-5102	171	18	svm	svm	NOUN
cana-5102	171	19	as	as	ADP
cana-5102	171	20	classifier	classifier	NOUN
cana-5102	171	21	.	.	PUNCT
cana-5102	172	1	it	it	PRON
cana-5102	172	2	gives	give	VERB
cana-5102	172	3	an	an	DET
cana-5102	172	4	accuracy	accuracy	NOUN
cana-5102	172	5	of	of	ADP
cana-5102	172	6	98.41	98.41	NUM
cana-5102	172	7	%	%	NOUN
cana-5102	172	8	for	for	ADP
cana-5102	172	9	cnn	cnn	PROPN
cana-5102	172	10	only	only	ADV
cana-5102	172	11	and	and	CCONJ
cana-5102	172	12	its	its	PRON
cana-5102	172	13	99	99	NUM
cana-5102	172	14	%	%	NOUN
cana-5102	172	15	for	for	ADP
cana-5102	172	16	both	both	DET
cana-5102	172	17	cnn	cnn	PROPN
cana-5102	172	18	and	and	CCONJ
cana-5102	172	19	svm.[34	svm.[34	PROPN
cana-5102	172	20	]	]	X
cana-5102	172	21	darshni	darshni	PROPN
cana-5102	172	22	et	et	PROPN
cana-5102	172	23	al	al	PROPN
cana-5102	172	24	.	.	PROPN
cana-5102	172	25	2023	2023	NUM
cana-5102	172	26	develops	develop	VERB
cana-5102	172	27	a	a	DET
cana-5102	172	28	character	character	NOUN
cana-5102	172	29	recognition	recognition	NOUN
cana-5102	172	30	system	system	NOUN
cana-5102	172	31	with	with	ADP
cana-5102	172	32	help	help	NOUN
cana-5102	172	33	of	of	ADP
cana-5102	172	34	scilab	scilab	PROPN
cana-5102	172	35	using	use	VERB
cana-5102	172	36	ann	ann	PROPN
cana-5102	172	37	to	to	PART
cana-5102	172	38	classify	classify	VERB
cana-5102	172	39	the	the	DET
cana-5102	172	40	classes	class	NOUN
cana-5102	172	41	.	.	PUNCT
cana-5102	173	1	the	the	DET
cana-5102	173	2	character	character	NOUN
cana-5102	173	3	used	use	VERB
cana-5102	173	4	dare	dare	VERB
cana-5102	173	5	numerals	numeral	NOUN
cana-5102	173	6	thus	thus	ADV
cana-5102	173	7	total	total	ADJ
cana-5102	173	8	classes	class	NOUN
cana-5102	173	9	to	to	PART
cana-5102	173	10	be	be	AUX
cana-5102	173	11	recognized	recognize	VERB
cana-5102	173	12	are	be	AUX
cana-5102	173	13	divided	divide	VERB
cana-5102	173	14	in	in	ADP
cana-5102	173	15	10	10	NUM
cana-5102	173	16	.	.	PUNCT
cana-5102	174	1	the	the	DET
cana-5102	174	2	classification	classification	NOUN
cana-5102	174	3	is	be	AUX
cana-5102	174	4	proceeded	proceed	VERB
cana-5102	174	5	by	by	ADP
cana-5102	174	6	online	online	ADJ
cana-5102	174	7	backpropagation	backpropagation	NOUN
cana-5102	174	8	with	with	ADP
cana-5102	174	9	help	help	NOUN
cana-5102	174	10	of	of	ADP
cana-5102	174	11	changing	change	VERB
cana-5102	174	12	weights	weight	NOUN
cana-5102	174	13	.	.	PUNCT
cana-5102	175	1	a	a	DET
cana-5102	175	2	2layer	2layer	NUM
cana-5102	175	3	ann	ann	PROPN
cana-5102	175	4	is	be	AUX
cana-5102	175	5	used	use	VERB
cana-5102	175	6	which	which	PRON
cana-5102	175	7	results	result	VERB
cana-5102	175	8	in	in	ADP
cana-5102	175	9	a	a	DET
cana-5102	175	10	lower	low	ADJ
cana-5102	175	11	error	error	NOUN
cana-5102	175	12	rate	rate	NOUN
cana-5102	175	13	and	and	CCONJ
cana-5102	175	14	accuracy	accuracy	NOUN
cana-5102	175	15	of	of	ADP
cana-5102	175	16	99.92	99.92	NUM
cana-5102	175	17	%	%	NOUN
cana-5102	175	18	.	.	PUNCT
cana-5102	176	1	with	with	ADP
cana-5102	176	2	standard	standard	ADJ
cana-5102	176	3	backpropagation	backpropagation	NOUN
cana-5102	176	4	the	the	DET
cana-5102	176	5	accuracy	accuracy	NOUN
cana-5102	176	6	came	come	VERB
cana-5102	176	7	to	to	PART
cana-5102	176	8	be	be	AUX
cana-5102	176	9	99.62	99.62	NUM
cana-5102	176	10	%	%	NOUN
cana-5102	176	11	.	.	PUNCT
cana-5102	177	1	[	[	X
cana-5102	177	2	35	35	NUM
cana-5102	177	3	]	]	PUNCT
cana-5102	177	4	from	from	ADP
cana-5102	177	5	all	all	DET
cana-5102	177	6	the	the	DET
cana-5102	177	7	above	above	ADJ
cana-5102	177	8	methods	method	NOUN
cana-5102	177	9	and	and	CCONJ
cana-5102	177	10	models	model	NOUN
cana-5102	177	11	,	,	PUNCT
cana-5102	177	12	we	we	PRON
cana-5102	177	13	get	get	VERB
cana-5102	177	14	to	to	PART
cana-5102	177	15	know	know	VERB
cana-5102	177	16	that	that	SCONJ
cana-5102	177	17	different	different	ADJ
cana-5102	177	18	models	model	NOUN
cana-5102	177	19	and	and	CCONJ
cana-5102	177	20	machine	machine	NOUN
cana-5102	177	21	learning	learning	NOUN
cana-5102	177	22	techniques	technique	NOUN
cana-5102	177	23	are	be	AUX
cana-5102	177	24	used	use	VERB
cana-5102	177	25	for	for	ADP
cana-5102	177	26	precise	precise	ADJ
cana-5102	177	27	character	character	NOUN
cana-5102	177	28	recognition	recognition	NOUN
cana-5102	177	29	.	.	PUNCT
cana-5102	178	1	some	some	DET
cana-5102	178	2	advanced	advanced	ADJ
cana-5102	178	3	techniques	technique	NOUN
cana-5102	178	4	like	like	ADP
cana-5102	178	5	cnn	cnn	PROPN
cana-5102	178	6	alone	alone	ADV
cana-5102	178	7	can	can	AUX
cana-5102	178	8	be	be	AUX
cana-5102	178	9	used	use	VERB
cana-5102	178	10	for	for	ADP
cana-5102	178	11	character	character	NOUN
cana-5102	178	12	recognition	recognition	NOUN
cana-5102	178	13	with	with	ADP
cana-5102	178	14	high	high	ADJ
cana-5102	178	15	accuracy	accuracy	NOUN
cana-5102	178	16	but	but	CCONJ
cana-5102	178	17	time	time	NOUN
cana-5102	178	18	consumption	consumption	NOUN
cana-5102	178	19	for	for	ADP
cana-5102	178	20	its	its	PRON
cana-5102	178	21	training	training	NOUN
cana-5102	178	22	and	and	CCONJ
cana-5102	178	23	prediction	prediction	NOUN
cana-5102	178	24	is	be	AUX
cana-5102	178	25	very	very	ADV
cana-5102	178	26	high	high	ADJ
cana-5102	178	27	.	.	PUNCT
cana-5102	179	1	in	in	ADP
cana-5102	179	2	fast	fast	ADJ
cana-5102	179	3	moving	move	VERB
cana-5102	179	4	world	world	NOUN
cana-5102	179	5	these	these	DET
cana-5102	179	6	techniques	technique	NOUN
cana-5102	179	7	remain	remain	VERB
cana-5102	179	8	behind	behind	ADP
cana-5102	179	9	the	the	DET
cana-5102	179	10	combinations	combination	NOUN
cana-5102	179	11	of	of	ADP
cana-5102	179	12	different	different	ADJ
cana-5102	179	13	feature	feature	NOUN
cana-5102	179	14	extraction	extraction	NOUN
cana-5102	179	15	and	and	CCONJ
cana-5102	179	16	classification	classification	NOUN
cana-5102	179	17	methods	method	NOUN
cana-5102	179	18	which	which	PRON
cana-5102	179	19	provide	provide	VERB
cana-5102	179	20	very	very	ADV
cana-5102	179	21	little	little	ADJ
cana-5102	179	22	complexity	complexity	NOUN
cana-5102	179	23	and	and	CCONJ
cana-5102	179	24	time	time	NOUN
cana-5102	179	25	communications	communication	NOUN
cana-5102	179	26	on	on	ADP
cana-5102	179	27	applied	apply	VERB
cana-5102	179	28	nonlinear	nonlinear	ADJ
cana-5102	179	29	analysis	analysis	NOUN
cana-5102	179	30	issn	issn	NOUN
cana-5102	179	31	:	:	PUNCT
cana-5102	179	32	1074	1074	NUM
cana-5102	179	33	-	-	PUNCT
cana-5102	179	34	133x	133x	NUM
cana-5102	179	35	vol	vol	NOUN
cana-5102	179	36	32	32	NUM
cana-5102	179	37	no	no	NOUN
cana-5102	179	38	.	.	PUNCT
cana-5102	180	1	icmasd	icmasd	NOUN
cana-5102	180	2	(	(	PUNCT
cana-5102	180	3	2025	2025	NUM
cana-5102	180	4	)	)	PUNCT
cana-5102	180	5	761	761	NUM
cana-5102	180	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	180	7	consumption	consumption	NOUN
cana-5102	180	8	for	for	ADP
cana-5102	180	9	high	high	ADJ
cana-5102	180	10	accuracy	accuracy	NOUN
cana-5102	180	11	.	.	PUNCT
cana-5102	181	1	table	table	NOUN
cana-5102	181	2	1	1	NUM
cana-5102	181	3	shows	show	VERB
cana-5102	181	4	different	different	ADJ
cana-5102	181	5	languages	language	NOUN
cana-5102	181	6	,	,	PUNCT
cana-5102	181	7	techniques	technique	NOUN
cana-5102	181	8	used	use	VERB
cana-5102	181	9	for	for	ADP
cana-5102	181	10	feature	feature	NOUN
cana-5102	181	11	extraction	extraction	NOUN
cana-5102	181	12	and	and	CCONJ
cana-5102	181	13	classification	classification	NOUN
cana-5102	181	14	model	model	NOUN
cana-5102	181	15	.	.	PUNCT
cana-5102	182	1	table	table	NOUN
cana-5102	182	2	1	1	NUM
cana-5102	182	3	:	:	PUNCT
cana-5102	182	4	comparison	comparison	NOUN
cana-5102	182	5	table	table	NOUN
cana-5102	182	6	ref	ref	NOUN
cana-5102	182	7	.	.	PUNCT
cana-5102	183	1	no	no	INTJ
cana-5102	183	2	.	.	PUNCT
cana-5102	184	1	language	language	NOUN
cana-5102	184	2	feature	feature	NOUN
cana-5102	184	3	extraction	extraction	NOUN
cana-5102	184	4	classification	classification	NOUN
cana-5102	184	5	result(%	result(%	NOUN
cana-5102	184	6	)	)	PUNCT
cana-5102	185	1	[	[	X
cana-5102	185	2	1	1	X
cana-5102	185	3	]	]	X
cana-5102	185	4	numeral	numeral	ADJ
cana-5102	185	5	direct	direct	ADJ
cana-5102	185	6	comparison	comparison	NOUN
cana-5102	185	7	ann	ann	PROPN
cana-5102	185	8	98.2	98.2	NUM
cana-5102	186	1	[	[	X
cana-5102	186	2	35	35	NUM
cana-5102	186	3	]	]	X
cana-5102	186	4	numeral	numeral	ADJ
cana-5102	186	5	ann	ann	PROPN
cana-5102	186	6	ann	ann	PROPN
cana-5102	186	7	99.92	99.92	NUM
cana-5102	186	8	[	[	X
cana-5102	186	9	31	31	NUM
cana-5102	186	10	]	]	X
cana-5102	186	11	devanagari	devanagari	NOUN
cana-5102	186	12	,	,	PUNCT
cana-5102	186	13	bangla	bangla	PROPN
cana-5102	186	14	relief	relief	NOUN
cana-5102	186	15	+	+	CCONJ
cana-5102	186	16	gray	gray	ADJ
cana-5102	186	17	wolf	wolf	PROPN
cana-5102	186	18	optimizer	optimizer	NOUN
cana-5102	186	19	cnn	cnn	PROPN
cana-5102	186	20	99.61,100	99.61,100	PROPN
cana-5102	186	21	(	(	PUNCT
cana-5102	186	22	nearly	nearly	ADV
cana-5102	186	23	)	)	PUNCT
cana-5102	187	1	[	[	X
cana-5102	187	2	33	33	NUM
cana-5102	187	3	]	]	X
cana-5102	187	4	devanagari	devanagari	PROPN
cana-5102	187	5	cnn	cnn	PROPN
cana-5102	187	6	cnn	cnn	PROPN
cana-5102	187	7	98.13	98.13	NUM
cana-5102	188	1	[	[	X
cana-5102	188	2	17	17	NUM
cana-5102	188	3	]	]	X
cana-5102	188	4	english	english	PROPN
cana-5102	188	5	freeman	freeman	PROPN
cana-5102	188	6	chain	chain	PROPN
cana-5102	188	7	code	code	NOUN
cana-5102	188	8	svm	svm	NOUN
cana-5102	188	9	88	88	NUM
cana-5102	189	1	[	[	SYM
cana-5102	189	2	23	23	NUM
cana-5102	189	3	]	]	PUNCT
cana-5102	189	4	arabic	arabic	ADJ
cana-5102	189	5	hog	hog	NOUN
cana-5102	189	6	svm	svm	PROPN
cana-5102	189	7	99	99	NUM
cana-5102	190	1	[	[	SYM
cana-5102	190	2	28	28	NUM
cana-5102	190	3	]	]	X
cana-5102	190	4	english	english	NOUN
cana-5102	190	5	+	+	CCONJ
cana-5102	190	6	numeral	numeral	ADJ
cana-5102	190	7	struct	struct	NOUN
cana-5102	190	8	.	.	PUNCT
cana-5102	191	1	+	+	PUNCT
cana-5102	191	2	temporal	temporal	ADJ
cana-5102	191	3	pattern	pattern	NOUN
cana-5102	191	4	svm	svm	NOUN
cana-5102	191	5	88	88	NUM
cana-5102	192	1	[	[	X
cana-5102	192	2	7	7	X
cana-5102	192	3	]	]	X
cana-5102	192	4	devnagari	devnagari	ADJ
cana-5102	192	5	density	density	NOUN
cana-5102	192	6	+	+	CCONJ
cana-5102	192	7	moment	moment	NOUN
cana-5102	192	8	feature	feature	NOUN
cana-5102	192	9	knn	knn	PROPN
cana-5102	192	10	89.68	89.68	NUM
cana-5102	193	1	[	[	X
cana-5102	193	2	16	16	NUM
cana-5102	193	3	]	]	X
cana-5102	193	4	english	english	ADJ
cana-5102	193	5	boundary	boundary	ADJ
cana-5102	193	6	tracer	tracer	NOUN
cana-5102	193	7	gradient	gradient	NOUN
cana-5102	193	8	decent	decent	ADJ
cana-5102	193	9	nn	nn	INTJ
cana-5102	193	10	94	94	NUM
cana-5102	194	1	[	[	SYM
cana-5102	194	2	18	18	NUM
cana-5102	194	3	]	]	PUNCT
cana-5102	194	4	hindi	hindi	NOUN
cana-5102	194	5	nn	nn	PROPN
cana-5102	194	6	gradient	gradient	PROPN
cana-5102	194	7	decent	decent	ADJ
cana-5102	194	8	nn	nn	INTJ
cana-5102	194	9	90	90	NUM
cana-5102	194	10	[	[	SYM
cana-5102	194	11	6	6	NUM
cana-5102	194	12	]	]	PUNCT
cana-5102	194	13	cursive	cursive	ADJ
cana-5102	194	14	global	global	ADJ
cana-5102	194	15	parameters	parameter	NOUN
cana-5102	194	16	hmm	hmm	ADV
cana-5102	194	17	88.3	88.3	NUM
cana-5102	194	18	[	[	X
cana-5102	194	19	26	26	NUM
cana-5102	194	20	]	]	X
cana-5102	194	21	chinese	chinese	ADJ
cana-5102	194	22	eight	eight	NUM
cana-5102	194	23	directional	directional	ADJ
cana-5102	194	24	ssdcnn	ssdcnn	PROPN
cana-5102	194	25	97.86	97.86	NUM
cana-5102	194	26	[	[	X
cana-5102	194	27	20	20	NUM
cana-5102	194	28	]	]	X
cana-5102	194	29	english	english	PROPN
cana-5102	194	30	ann	ann	PROPN
cana-5102	194	31	,	,	PUNCT
cana-5102	194	32	nn	nn	PROPN
cana-5102	194	33	57.69	57.69	NUM
cana-5102	194	34	,	,	PUNCT
cana-5102	194	35	61.53	61.53	NUM
cana-5102	194	36	[	[	X
cana-5102	194	37	34	34	NUM
cana-5102	194	38	]	]	X
cana-5102	194	39	urdu	urdu	PROPN
cana-5102	194	40	cnn	cnn	PROPN
cana-5102	194	41	cnn	cnn	PROPN
cana-5102	194	42	+	+	CCONJ
cana-5102	194	43	svm	svm	PROPN
cana-5102	194	44	99	99	NUM
cana-5102	195	1	[	[	SYM
cana-5102	195	2	22	22	NUM
cana-5102	195	3	]	]	X
cana-5102	195	4	arabic	arabic	ADJ
cana-5102	195	5	structural	structural	PROPN
cana-5102	195	6	characteristics	characteristics	PROPN
cana-5102	195	7	ffd	ffd	PROPN
cana-5102	195	8	-	-	PUNCT
cana-5102	195	9	nn	nn	PROPN
cana-5102	195	10	98.27	98.27	NUM
cana-5102	195	11	[	[	X
cana-5102	195	12	9	9	NUM
cana-5102	195	13	]	]	X
cana-5102	195	14	english	english	ADJ
cana-5102	195	15	geometric	geometric	NOUN
cana-5102	195	16	features	feature	VERB
cana-5102	195	17	fuzzy	fuzzy	ADJ
cana-5102	195	18	logic	logic	NOUN
cana-5102	195	19	90	90	NUM
cana-5102	196	1	[	[	SYM
cana-5102	196	2	10	10	NUM
cana-5102	196	3	]	]	X
cana-5102	196	4	chinese	chinese	ADJ
cana-5102	196	5	euclidean	euclidean	ADJ
cana-5102	196	6	distance	distance	NOUN
cana-5102	196	7	glvq	glvq	ADV
cana-5102	196	8	+	+	CCONJ
cana-5102	196	9	fda	fda	PROPN
cana-5102	196	10	99.6	99.6	NUM
cana-5102	196	11	[	[	X
cana-5102	196	12	2	2	NUM
cana-5102	196	13	]	]	PUNCT
cana-5102	196	14	hindi	hindi	NOUN
cana-5102	196	15	hmm	hmm	INTJ
cana-5102	197	1	+	+	PROPN
cana-5102	197	2	nn	nn	PROPN
cana-5102	197	3	86.5	86.5	NUM
cana-5102	197	4	[	[	NOUN
cana-5102	197	5	24	24	NUM
cana-5102	197	6	]	]	X
cana-5102	197	7	english	english	PROPN
cana-5102	197	8	fft	fft	PROPN
cana-5102	197	9	+	+	CCONJ
cana-5102	197	10	wavelet	wavelet	PROPN
cana-5102	197	11	knn	knn	PROPN
cana-5102	197	12	,	,	PUNCT
cana-5102	197	13	lda	lda	PROPN
cana-5102	197	14	,	,	PUNCT
cana-5102	197	15	lstm	lstm	NOUN
cana-5102	197	16	95,92,84	95,92,84	NUM
cana-5102	197	17	[	[	X
cana-5102	197	18	19	19	NUM
cana-5102	197	19	]	]	PUNCT
cana-5102	197	20	hindi	hindi	NOUN
cana-5102	197	21	euclidean	euclidean	PROPN
cana-5102	197	22	distance	distance	PROPN
cana-5102	197	23	knn	knn	PROPN
cana-5102	198	1	+	+	CCONJ
cana-5102	198	2	k	k	X
cana-5102	198	3	-	-	PUNCT
cana-5102	198	4	means	mean	VERB
cana-5102	198	5	95.86	95.86	NUM
cana-5102	199	1	[	[	X
cana-5102	199	2	32	32	NUM
cana-5102	199	3	]	]	X
cana-5102	199	4	english	english	PROPN
cana-5102	199	5	cnn	cnn	PROPN
cana-5102	199	6	lstm	lstm	PROPN
cana-5102	199	7	+	+	CCONJ
cana-5102	199	8	cnn	cnn	PROPN
cana-5102	199	9	98.31	98.31	NUM
cana-5102	200	1	[	[	X
cana-5102	200	2	21	21	NUM
cana-5102	200	3	]	]	X
cana-5102	200	4	devanagari	devanagari	ADJ
cana-5102	200	5	piecewise	piecewise	PROPN
cana-5102	200	6	hog	hog	NOUN
cana-5102	200	7	ann	ann	PROPN
cana-5102	200	8	99.27	99.27	NUM
cana-5102	201	1	[	[	X
cana-5102	201	2	15	15	NUM
cana-5102	201	3	]	]	X
cana-5102	201	4	numeral	numeral	ADJ
cana-5102	201	5	fourier	fourier	NOUN
cana-5102	201	6	descriptor	descriptor	NOUN
cana-5102	201	7	ann	ann	PROPN
cana-5102	201	8	97	97	NUM
cana-5102	202	1	[	[	X
cana-5102	202	2	13	13	NUM
cana-5102	202	3	]	]	X
cana-5102	202	4	indic	indic	ADJ
cana-5102	202	5	numeral	numeral	ADJ
cana-5102	202	6	directional	directional	ADJ
cana-5102	202	7	chain	chain	NOUN
cana-5102	202	8	code	code	NOUN
cana-5102	202	9	quadratic	quadratic	ADJ
cana-5102	202	10	classifier	classifier	NOUN
cana-5102	202	11	99.56	99.56	NUM
cana-5102	202	12	[	[	X
cana-5102	202	13	3	3	NUM
cana-5102	202	14	]	]	X
cana-5102	202	15	tamil	tamil	NOUN
cana-5102	202	16	,	,	PUNCT
cana-5102	202	17	english	english	PROPN
cana-5102	202	18	svm	svm	PROPN
cana-5102	202	19	nn	nn	PROPN
cana-5102	202	20	knn	knn	PROPN
cana-5102	202	21	88.4	88.4	NUM
cana-5102	202	22	,	,	PUNCT
cana-5102	202	23	87.7	87.7	NUM
cana-5102	202	24	73.6	73.6	NUM
cana-5102	202	25	,	,	PUNCT
cana-5102	202	26	71.2	71.2	NUM
cana-5102	202	27	68.2	68.2	NUM
cana-5102	202	28	,	,	PUNCT
cana-5102	202	29	84.7	84.7	NUM
cana-5102	202	30	[	[	SYM
cana-5102	202	31	14	14	NUM
cana-5102	202	32	]	]	X
cana-5102	202	33	devanagari	devanagari	ADJ
cana-5102	202	34	directional	directional	ADJ
cana-5102	202	35	+	+	CCONJ
cana-5102	202	36	curvature	curvature	NOUN
cana-5102	202	37	svm	svm	NOUN
cana-5102	202	38	+	+	CCONJ
cana-5102	202	39	mqdf	mqdf	ADJ
cana-5102	202	40	95.13	95.13	NUM
cana-5102	202	41	communications	communication	NOUN
cana-5102	202	42	on	on	ADP
cana-5102	202	43	applied	apply	VERB
cana-5102	202	44	nonlinear	nonlinear	ADJ
cana-5102	202	45	analysis	analysis	NOUN
cana-5102	202	46	issn	issn	NOUN
cana-5102	202	47	:	:	PUNCT
cana-5102	202	48	1074	1074	NUM
cana-5102	202	49	-	-	PUNCT
cana-5102	202	50	133x	133x	NUM
cana-5102	202	51	vol	vol	NOUN
cana-5102	202	52	32	32	NUM
cana-5102	202	53	no	no	NOUN
cana-5102	202	54	.	.	PUNCT
cana-5102	203	1	icmasd	icmasd	NOUN
cana-5102	203	2	(	(	PUNCT
cana-5102	203	3	2025	2025	NUM
cana-5102	203	4	)	)	PUNCT
cana-5102	203	5	762	762	NUM
cana-5102	203	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	203	7	methodology	methodology	NOUN
cana-5102	203	8	:	:	PUNCT
cana-5102	203	9	fig	fig	NOUN
cana-5102	203	10	1	1	NUM
cana-5102	203	11	:	:	PUNCT
cana-5102	203	12	training	training	NOUN
cana-5102	203	13	of	of	ADP
cana-5102	203	14	a	a	DET
cana-5102	203	15	model	model	NOUN
cana-5102	203	16	the	the	DET
cana-5102	203	17	research	research	NOUN
cana-5102	203	18	methodology	methodology	NOUN
cana-5102	203	19	adopted	adopt	VERB
cana-5102	203	20	in	in	ADP
cana-5102	203	21	this	this	DET
cana-5102	203	22	study	study	NOUN
cana-5102	203	23	employs	employ	VERB
cana-5102	203	24	a	a	DET
cana-5102	203	25	structured	structured	ADJ
cana-5102	203	26	framework	framework	NOUN
cana-5102	203	27	to	to	PART
cana-5102	203	28	assess	assess	VERB
cana-5102	203	29	and	and	CCONJ
cana-5102	203	30	contrast	contrast	VERB
cana-5102	203	31	the	the	DET
cana-5102	203	32	efficacy	efficacy	NOUN
cana-5102	203	33	of	of	ADP
cana-5102	203	34	various	various	ADJ
cana-5102	203	35	machine	machine	NOUN
cana-5102	203	36	learning	learning	NOUN
cana-5102	203	37	and	and	CCONJ
cana-5102	203	38	deep	deep	ADJ
cana-5102	203	39	learning	learning	NOUN
cana-5102	203	40	models	model	NOUN
cana-5102	203	41	in	in	ADP
cana-5102	203	42	the	the	DET
cana-5102	203	43	context	context	NOUN
cana-5102	203	44	of	of	ADP
cana-5102	203	45	optical	optical	ADJ
cana-5102	203	46	character	character	NOUN
cana-5102	203	47	recognition	recognition	NOUN
cana-5102	203	48	(	(	PUNCT
cana-5102	203	49	ocr	ocr	ADJ
cana-5102	203	50	)	)	PUNCT
cana-5102	203	51	tasks	task	NOUN
cana-5102	203	52	.	.	PUNCT
cana-5102	204	1	this	this	DET
cana-5102	204	2	framework	framework	NOUN
cana-5102	204	3	is	be	AUX
cana-5102	204	4	organized	organize	VERB
cana-5102	204	5	into	into	ADP
cana-5102	204	6	several	several	ADJ
cana-5102	204	7	critical	critical	ADJ
cana-5102	204	8	phases	phase	NOUN
cana-5102	204	9	,	,	PUNCT
cana-5102	204	10	which	which	PRON
cana-5102	204	11	include	include	VERB
cana-5102	204	12	data	datum	NOUN
cana-5102	204	13	preparation	preparation	NOUN
cana-5102	204	14	,	,	PUNCT
cana-5102	204	15	model	model	NOUN
cana-5102	204	16	selection	selection	NOUN
cana-5102	204	17	,	,	PUNCT
cana-5102	204	18	training	training	NOUN
cana-5102	204	19	,	,	PUNCT
cana-5102	204	20	evaluation	evaluation	NOUN
cana-5102	204	21	,	,	PUNCT
cana-5102	204	22	and	and	CCONJ
cana-5102	204	23	comparative	comparative	ADJ
cana-5102	204	24	analysis	analysis	NOUN
cana-5102	204	25	.	.	PUNCT
cana-5102	205	1	1	1	X
cana-5102	205	2	.	.	X
cana-5102	205	3	data	datum	NOUN
cana-5102	205	4	preparation	preparation	NOUN
cana-5102	205	5	•	•	NOUN
cana-5102	205	6	two	two	NUM
cana-5102	205	7	distinct	distinct	ADJ
cana-5102	205	8	datasets	dataset	NOUN
cana-5102	205	9	characterized	characterize	VERB
cana-5102	205	10	by	by	ADP
cana-5102	205	11	different	different	ADJ
cana-5102	205	12	resolutions	resolution	NOUN
cana-5102	205	13	(	(	PUNCT
cana-5102	205	14	64x	64x	NOUN
cana-5102	205	15	and	and	CCONJ
cana-5102	205	16	28x	28x	NOUN
cana-5102	205	17	pixels	pixel	NOUN
cana-5102	205	18	)	)	PUNCT
cana-5102	205	19	are	be	AUX
cana-5102	205	20	chosen	choose	VERB
cana-5102	205	21	to	to	PART
cana-5102	205	22	reflect	reflect	VERB
cana-5102	205	23	diverse	diverse	ADJ
cana-5102	205	24	image	image	NOUN
cana-5102	205	25	quality	quality	NOUN
cana-5102	205	26	levels	level	NOUN
cana-5102	205	27	.	.	PUNCT
cana-5102	206	1	the	the	DET
cana-5102	206	2	images	image	NOUN
cana-5102	206	3	undergo	undergo	VERB
cana-5102	206	4	a	a	DET
cana-5102	206	5	series	series	NOUN
cana-5102	206	6	of	of	ADP
cana-5102	206	7	preprocessing	preprocesse	VERB
cana-5102	206	8	steps	step	NOUN
cana-5102	206	9	,	,	PUNCT
cana-5102	206	10	which	which	PRON
cana-5102	206	11	encompass	encompass	VERB
cana-5102	206	12	normalization	normalization	NOUN
cana-5102	206	13	and	and	CCONJ
cana-5102	206	14	label	label	NOUN
cana-5102	206	15	encoding	encoding	NOUN
cana-5102	206	16	.	.	PUNCT
cana-5102	207	1	to	to	PART
cana-5102	207	2	enhance	enhance	VERB
cana-5102	207	3	the	the	DET
cana-5102	207	4	robustness	robustness	NOUN
cana-5102	207	5	of	of	ADP
cana-5102	207	6	the	the	DET
cana-5102	207	7	models	model	NOUN
cana-5102	207	8	,	,	PUNCT
cana-5102	207	9	data	datum	NOUN
cana-5102	207	10	augmentation	augmentation	NOUN
cana-5102	207	11	techniques	technique	NOUN
cana-5102	207	12	such	such	ADJ
cana-5102	207	13	as	as	ADP
cana-5102	207	14	rotation	rotation	NOUN
cana-5102	207	15	,	,	PUNCT
cana-5102	207	16	scaling	scaling	NOUN
cana-5102	207	17	,	,	PUNCT
cana-5102	207	18	and	and	CCONJ
cana-5102	207	19	the	the	DET
cana-5102	207	20	introduction	introduction	NOUN
cana-5102	207	21	of	of	ADP
cana-5102	207	22	noise	noise	NOUN
cana-5102	207	23	may	may	AUX
cana-5102	207	24	be	be	AUX
cana-5102	207	25	utilized	utilize	VERB
cana-5102	207	26	.	.	PUNCT
cana-5102	208	1	subsequently	subsequently	ADV
cana-5102	208	2	,	,	PUNCT
cana-5102	208	3	the	the	DET
cana-5102	208	4	datasets	dataset	NOUN
cana-5102	208	5	are	be	AUX
cana-5102	208	6	partitioned	partition	VERB
cana-5102	208	7	into	into	ADP
cana-5102	208	8	training	training	NOUN
cana-5102	208	9	,	,	PUNCT
cana-5102	208	10	validation	validation	NOUN
cana-5102	208	11	,	,	PUNCT
cana-5102	208	12	and	and	CCONJ
cana-5102	208	13	test	test	NOUN
cana-5102	208	14	subsets	subset	NOUN
cana-5102	208	15	to	to	PART
cana-5102	208	16	facilitate	facilitate	VERB
cana-5102	208	17	an	an	DET
cana-5102	208	18	unbiased	unbiased	ADJ
cana-5102	208	19	assessment	assessment	NOUN
cana-5102	208	20	.	.	PUNCT
cana-5102	209	1	2	2	X
cana-5102	209	2	.	.	X
cana-5102	209	3	model	model	NOUN
cana-5102	209	4	selection	selection	NOUN
cana-5102	209	5	•	•	ADP
cana-5102	209	6	a	a	DET
cana-5102	209	7	variety	variety	NOUN
cana-5102	209	8	of	of	ADP
cana-5102	209	9	convolutional	convolutional	ADJ
cana-5102	209	10	neural	neural	ADJ
cana-5102	209	11	network	network	NOUN
cana-5102	209	12	(	(	PUNCT
cana-5102	209	13	cnn	cnn	PROPN
cana-5102	209	14	)	)	PUNCT
cana-5102	209	15	architectures	architecture	NOUN
cana-5102	209	16	are	be	AUX
cana-5102	209	17	developed	develop	VERB
cana-5102	209	18	,	,	PUNCT
cana-5102	209	19	each	each	PRON
cana-5102	209	20	featuring	feature	VERB
cana-5102	209	21	unique	unique	ADJ
cana-5102	209	22	configurations	configuration	NOUN
cana-5102	209	23	that	that	PRON
cana-5102	209	24	include	include	VERB
cana-5102	209	25	differences	difference	NOUN
cana-5102	209	26	in	in	ADP
cana-5102	209	27	the	the	DET
cana-5102	209	28	number	number	NOUN
cana-5102	209	29	of	of	ADP
cana-5102	209	30	convolutional	convolutional	ADJ
cana-5102	209	31	layers	layer	NOUN
cana-5102	209	32	,	,	PUNCT
cana-5102	209	33	activation	activation	NOUN
cana-5102	209	34	functions	function	NOUN
cana-5102	209	35	,	,	PUNCT
cana-5102	209	36	and	and	CCONJ
cana-5102	209	37	optimization	optimization	NOUN
cana-5102	209	38	algorithms	algorithm	NOUN
cana-5102	209	39	.	.	PUNCT
cana-5102	210	1	traditional	traditional	ADJ
cana-5102	210	2	machine	machine	NOUN
cana-5102	210	3	learning	learning	NOUN
cana-5102	210	4	models	model	NOUN
cana-5102	210	5	,	,	PUNCT
cana-5102	210	6	such	such	ADJ
cana-5102	210	7	as	as	ADP
cana-5102	210	8	support	support	NOUN
cana-5102	210	9	vector	vector	NOUN
cana-5102	210	10	machines	machine	NOUN
cana-5102	210	11	(	(	PUNCT
cana-5102	210	12	svm	svm	PROPN
cana-5102	210	13	)	)	PUNCT
cana-5102	210	14	and	and	CCONJ
cana-5102	210	15	random	random	ADJ
cana-5102	210	16	forests	forest	NOUN
cana-5102	210	17	,	,	PUNCT
cana-5102	210	18	are	be	AUX
cana-5102	210	19	also	also	ADV
cana-5102	210	20	employed	employ	VERB
cana-5102	210	21	for	for	ADP
cana-5102	210	22	comparative	comparative	ADJ
cana-5102	210	23	purposes	purpose	NOUN
cana-5102	210	24	.	.	PUNCT
cana-5102	211	1	furthermore	furthermore	ADV
cana-5102	211	2	,	,	PUNCT
cana-5102	211	3	sequence	sequence	NOUN
cana-5102	211	4	-	-	PUNCT
cana-5102	211	5	based	base	VERB
cana-5102	211	6	models	model	NOUN
cana-5102	211	7	,	,	PUNCT
cana-5102	211	8	including	include	VERB
cana-5102	211	9	long	long	ADJ
cana-5102	211	10	short	short	ADJ
cana-5102	211	11	-	-	PUNCT
cana-5102	211	12	term	term	NOUN
cana-5102	211	13	memory	memory	NOUN
cana-5102	211	14	(	(	PUNCT
cana-5102	211	15	lstm	lstm	NOUN
cana-5102	211	16	)	)	PUNCT
cana-5102	211	17	networks	network	NOUN
cana-5102	211	18	,	,	PUNCT
cana-5102	211	19	are	be	AUX
cana-5102	211	20	investigated	investigate	VERB
cana-5102	211	21	for	for	ADP
cana-5102	211	22	their	their	PRON
cana-5102	211	23	capabilities	capability	NOUN
cana-5102	211	24	in	in	ADP
cana-5102	211	25	processing	process	VERB
cana-5102	211	26	sequential	sequential	ADJ
cana-5102	211	27	data	datum	NOUN
cana-5102	211	28	.	.	PUNCT
cana-5102	212	1	additionally	additionally	ADV
cana-5102	212	2	,	,	PUNCT
cana-5102	212	3	hybrid	hybrid	ADJ
cana-5102	212	4	models	model	NOUN
cana-5102	212	5	that	that	PRON
cana-5102	212	6	integrate	integrate	VERB
cana-5102	212	7	encodercommunications	encodercommunication	NOUN
cana-5102	212	8	on	on	ADP
cana-5102	212	9	applied	apply	VERB
cana-5102	212	10	nonlinear	nonlinear	ADJ
cana-5102	212	11	analysis	analysis	NOUN
cana-5102	212	12	issn	issn	NOUN
cana-5102	212	13	:	:	PUNCT
cana-5102	212	14	1074	1074	NUM
cana-5102	212	15	-	-	PUNCT
cana-5102	212	16	133x	133x	NUM
cana-5102	212	17	vol	vol	NOUN
cana-5102	212	18	32	32	NUM
cana-5102	212	19	no	no	NOUN
cana-5102	212	20	.	.	PUNCT
cana-5102	213	1	icmasd	icmasd	NOUN
cana-5102	213	2	(	(	PUNCT
cana-5102	213	3	2025	2025	NUM
cana-5102	213	4	)	)	PUNCT
cana-5102	213	5	763	763	NUM
cana-5102	214	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	214	2	decoder	decoder	NOUN
cana-5102	214	3	frameworks	framework	NOUN
cana-5102	214	4	are	be	AUX
cana-5102	214	5	evaluated	evaluate	VERB
cana-5102	214	6	to	to	PART
cana-5102	214	7	capitalize	capitalize	VERB
cana-5102	214	8	on	on	ADP
cana-5102	214	9	their	their	PRON
cana-5102	214	10	advantages	advantage	NOUN
cana-5102	214	11	in	in	ADP
cana-5102	214	12	feature	feature	NOUN
cana-5102	214	13	extraction	extraction	NOUN
cana-5102	214	14	and	and	CCONJ
cana-5102	214	15	sequence	sequence	NOUN
cana-5102	214	16	prediction	prediction	NOUN
cana-5102	214	17	.	.	PUNCT
cana-5102	215	1	3	3	X
cana-5102	215	2	.	.	X
cana-5102	215	3	training	train	VERB
cana-5102	215	4	•	•	NOUN
cana-5102	215	5	the	the	DET
cana-5102	215	6	process	process	NOUN
cana-5102	215	7	of	of	ADP
cana-5102	215	8	hyperparameter	hyperparameter	NOUN
cana-5102	215	9	tuning	tuning	NOUN
cana-5102	215	10	is	be	AUX
cana-5102	215	11	conducted	conduct	VERB
cana-5102	215	12	to	to	PART
cana-5102	215	13	enhance	enhance	VERB
cana-5102	215	14	model	model	NOUN
cana-5102	215	15	performance	performance	NOUN
cana-5102	215	16	,	,	PUNCT
cana-5102	215	17	taking	take	VERB
cana-5102	215	18	into	into	ADP
cana-5102	215	19	account	account	NOUN
cana-5102	215	20	variables	variable	NOUN
cana-5102	215	21	such	such	ADJ
cana-5102	215	22	as	as	ADP
cana-5102	215	23	dropout	dropout	NOUN
cana-5102	215	24	rates	rate	NOUN
cana-5102	215	25	,	,	PUNCT
cana-5102	215	26	the	the	DET
cana-5102	215	27	number	number	NOUN
cana-5102	215	28	of	of	ADP
cana-5102	215	29	dense	dense	ADJ
cana-5102	215	30	units	unit	NOUN
cana-5102	215	31	,	,	PUNCT
cana-5102	215	32	and	and	CCONJ
cana-5102	215	33	the	the	DET
cana-5102	215	34	selection	selection	NOUN
cana-5102	215	35	of	of	ADP
cana-5102	215	36	optimizers	optimizer	NOUN
cana-5102	215	37	.	.	PUNCT
cana-5102	216	1	each	each	DET
cana-5102	216	2	model	model	NOUN
cana-5102	216	3	is	be	AUX
cana-5102	216	4	trained	train	VERB
cana-5102	216	5	using	use	VERB
cana-5102	216	6	the	the	DET
cana-5102	216	7	prepared	prepared	ADJ
cana-5102	216	8	datasets	dataset	NOUN
cana-5102	216	9	,	,	PUNCT
cana-5102	216	10	with	with	ADP
cana-5102	216	11	validation	validation	NOUN
cana-5102	216	12	accuracy	accuracy	NOUN
cana-5102	216	13	being	be	AUX
cana-5102	216	14	closely	closely	ADV
cana-5102	216	15	monitored	monitor	VERB
cana-5102	216	16	to	to	PART
cana-5102	216	17	mitigate	mitigate	VERB
cana-5102	216	18	the	the	DET
cana-5102	216	19	risk	risk	NOUN
cana-5102	216	20	of	of	ADP
cana-5102	216	21	overfitting	overfitte	VERB
cana-5102	216	22	.	.	PUNCT
cana-5102	217	1	an	an	DET
cana-5102	217	2	early	early	ADJ
cana-5102	217	3	stopping	stopping	NOUN
cana-5102	217	4	mechanism	mechanism	NOUN
cana-5102	217	5	is	be	AUX
cana-5102	217	6	implemented	implement	VERB
cana-5102	217	7	to	to	PART
cana-5102	217	8	halt	halt	VERB
cana-5102	217	9	training	training	NOUN
cana-5102	217	10	if	if	SCONJ
cana-5102	217	11	there	there	PRON
cana-5102	217	12	is	be	VERB
cana-5102	217	13	no	no	DET
cana-5102	217	14	improvement	improvement	NOUN
cana-5102	217	15	in	in	ADP
cana-5102	217	16	validation	validation	NOUN
cana-5102	217	17	accuracy	accuracy	NOUN
cana-5102	217	18	over	over	ADP
cana-5102	217	19	a	a	DET
cana-5102	217	20	predetermined	predetermine	VERB
cana-5102	217	21	number	number	NOUN
cana-5102	217	22	of	of	ADP
cana-5102	217	23	epochs	epoch	NOUN
cana-5102	217	24	,	,	PUNCT
cana-5102	217	25	thereby	thereby	ADV
cana-5102	217	26	ensuring	ensure	VERB
cana-5102	217	27	a	a	DET
cana-5102	217	28	more	more	ADV
cana-5102	217	29	efficient	efficient	ADJ
cana-5102	217	30	training	training	NOUN
cana-5102	217	31	process	process	NOUN
cana-5102	217	32	.	.	PUNCT
cana-5102	218	1	4	4	X
cana-5102	218	2	.	.	X
cana-5102	218	3	evaluation	evaluation	NOUN
cana-5102	218	4	•	•	ADP
cana-5102	218	5	the	the	DET
cana-5102	218	6	primary	primary	ADJ
cana-5102	218	7	metric	metric	NOUN
cana-5102	218	8	employed	employ	VERB
cana-5102	218	9	to	to	PART
cana-5102	218	10	evaluate	evaluate	VERB
cana-5102	218	11	model	model	NOUN
cana-5102	218	12	performance	performance	NOUN
cana-5102	218	13	is	be	AUX
cana-5102	218	14	validation	validation	NOUN
cana-5102	218	15	accuracy	accuracy	NOUN
cana-5102	218	16	.	.	PUNCT
cana-5102	219	1	additionally	additionally	ADV
cana-5102	219	2	,	,	PUNCT
cana-5102	219	3	training	training	NOUN
cana-5102	219	4	and	and	CCONJ
cana-5102	219	5	validation	validation	NOUN
cana-5102	219	6	loss	loss	NOUN
cana-5102	219	7	are	be	AUX
cana-5102	219	8	tracked	track	VERB
cana-5102	219	9	to	to	PART
cana-5102	219	10	gain	gain	VERB
cana-5102	219	11	insights	insight	NOUN
cana-5102	219	12	into	into	ADP
cana-5102	219	13	the	the	DET
cana-5102	219	14	learning	learning	NOUN
cana-5102	219	15	process	process	NOUN
cana-5102	219	16	and	and	CCONJ
cana-5102	219	17	to	to	PART
cana-5102	219	18	identify	identify	VERB
cana-5102	219	19	potential	potential	ADJ
cana-5102	219	20	challenges	challenge	NOUN
cana-5102	219	21	such	such	ADJ
cana-5102	219	22	as	as	ADP
cana-5102	219	23	underfitting	underfitting	NOUN
cana-5102	219	24	or	or	CCONJ
cana-5102	219	25	overfitting	overfitting	NOUN
cana-5102	219	26	.	.	PUNCT
cana-5102	220	1	in	in	ADP
cana-5102	220	2	certain	certain	ADJ
cana-5102	220	3	instances	instance	NOUN
cana-5102	220	4	,	,	PUNCT
cana-5102	220	5	confusion	confusion	NOUN
cana-5102	220	6	matrices	matrix	NOUN
cana-5102	220	7	are	be	AUX
cana-5102	220	8	utilized	utilize	VERB
cana-5102	220	9	to	to	PART
cana-5102	220	10	conduct	conduct	VERB
cana-5102	220	11	a	a	DET
cana-5102	220	12	comprehensive	comprehensive	ADJ
cana-5102	220	13	analysis	analysis	NOUN
cana-5102	220	14	of	of	ADP
cana-5102	220	15	classification	classification	NOUN
cana-5102	220	16	performance	performance	NOUN
cana-5102	220	17	,	,	PUNCT
cana-5102	220	18	thereby	thereby	ADV
cana-5102	220	19	revealing	reveal	VERB
cana-5102	220	20	patterns	pattern	NOUN
cana-5102	220	21	of	of	ADP
cana-5102	220	22	misclassification	misclassification	NOUN
cana-5102	220	23	.	.	PUNCT
cana-5102	221	1	5	5	X
cana-5102	221	2	.	.	X
cana-5102	221	3	comparison	comparison	NOUN
cana-5102	221	4	•	•	ADP
cana-5102	221	5	the	the	DET
cana-5102	221	6	performance	performance	NOUN
cana-5102	221	7	of	of	ADP
cana-5102	221	8	various	various	ADJ
cana-5102	221	9	models	model	NOUN
cana-5102	221	10	is	be	AUX
cana-5102	221	11	assessed	assess	VERB
cana-5102	221	12	by	by	ADP
cana-5102	221	13	examining	examine	VERB
cana-5102	221	14	validation	validation	NOUN
cana-5102	221	15	accuracy	accuracy	NOUN
cana-5102	221	16	,	,	PUNCT
cana-5102	221	17	training	training	NOUN
cana-5102	221	18	duration	duration	NOUN
cana-5102	221	19	,	,	PUNCT
cana-5102	221	20	and	and	CCONJ
cana-5102	221	21	complexity	complexity	NOUN
cana-5102	221	22	.	.	PUNCT
cana-5102	222	1	furthermore	furthermore	ADV
cana-5102	222	2	,	,	PUNCT
cana-5102	222	3	the	the	DET
cana-5102	222	4	influence	influence	NOUN
cana-5102	222	5	of	of	ADP
cana-5102	222	6	dataset	dataset	ADJ
cana-5102	222	7	resolution	resolution	NOUN
cana-5102	222	8	on	on	ADP
cana-5102	222	9	model	model	NOUN
cana-5102	222	10	efficacy	efficacy	NOUN
cana-5102	222	11	is	be	AUX
cana-5102	222	12	investigated	investigate	VERB
cana-5102	222	13	by	by	ADP
cana-5102	222	14	contrasting	contrast	VERB
cana-5102	222	15	outcomes	outcome	NOUN
cana-5102	222	16	from	from	ADP
cana-5102	222	17	the	the	DET
cana-5102	222	18	64x	64x	NOUN
cana-5102	222	19	and	and	CCONJ
cana-5102	222	20	28x	28x	NOUN
cana-5102	222	21	datasets	dataset	NOUN
cana-5102	222	22	.	.	PUNCT
cana-5102	223	1	this	this	DET
cana-5102	223	2	analysis	analysis	NOUN
cana-5102	223	3	aids	aid	NOUN
cana-5102	223	4	in	in	ADP
cana-5102	223	5	discerning	discern	VERB
cana-5102	223	6	which	which	DET
cana-5102	223	7	models	model	NOUN
cana-5102	223	8	excel	excel	VERB
cana-5102	223	9	under	under	ADP
cana-5102	223	10	varying	vary	VERB
cana-5102	223	11	conditions	condition	NOUN
cana-5102	223	12	and	and	CCONJ
cana-5102	223	13	in	in	ADP
cana-5102	223	14	establishing	establish	VERB
cana-5102	223	15	the	the	DET
cana-5102	223	16	most	most	ADV
cana-5102	223	17	effective	effective	ADJ
cana-5102	223	18	strategies	strategy	NOUN
cana-5102	223	19	for	for	ADP
cana-5102	223	20	ocr	ocr	ADJ
cana-5102	223	21	tasks	task	NOUN
cana-5102	223	22	.	.	PUNCT
cana-5102	224	1	experiment	experiment	NOUN
cana-5102	224	2	and	and	CCONJ
cana-5102	224	3	results	result	NOUN
cana-5102	224	4	:	:	PUNCT
cana-5102	224	5	table	table	NOUN
cana-5102	224	6	2	2	NUM
cana-5102	224	7	:	:	PUNCT
cana-5102	224	8	experiment	experiment	NOUN
cana-5102	224	9	result	result	NOUN
cana-5102	224	10	of	of	ADP
cana-5102	224	11	different	different	ADJ
cana-5102	224	12	models	model	NOUN
cana-5102	224	13	for	for	ADP
cana-5102	224	14	64x	64x	PRON
cana-5102	224	15	data	datum	NOUN
cana-5102	224	16	and	and	CCONJ
cana-5102	224	17	28x	28x	NOUN
cana-5102	224	18	data	datum	NOUN
cana-5102	224	19	model	model	NOUN
cana-5102	224	20	type	type	NOUN
cana-5102	224	21	best	good	ADJ
cana-5102	224	22	model	model	NOUN
cana-5102	224	23	(	(	PUNCT
cana-5102	224	24	64x	64x	NOUN
cana-5102	224	25	)	)	PUNCT
cana-5102	224	26	accura	accura	NOUN
cana-5102	225	1	cy	cy	PROPN
cana-5102	225	2	(	(	PUNCT
cana-5102	225	3	64x	64x	NOUN
cana-5102	225	4	)	)	PUNCT
cana-5102	225	5	best	good	ADJ
cana-5102	225	6	model	model	NOUN
cana-5102	225	7	(	(	PUNCT
cana-5102	225	8	28x	28x	NOUN
cana-5102	225	9	)	)	PUNCT
cana-5102	225	10	accur	accur	PROPN
cana-5102	225	11	acy	acy	PROPN
cana-5102	225	12	(	(	PUNCT
cana-5102	225	13	28x	28x	NOUN
cana-5102	225	14	)	)	PUNCT
cana-5102	225	15	remarks	remark	VERB
cana-5102	225	16	cnn	cnn	PROPN
cana-5102	225	17	model	model	VERB
cana-5102	225	18	1	1	NUM
cana-5102	225	19	0.9882	0.9882	NUM
cana-5102	225	20	model	model	NOUN
cana-5102	225	21	10	10	NUM
cana-5102	225	22	0.976	0.976	NUM
cana-5102	225	23	3	3	NUM
cana-5102	225	24	cnns	cnn	NOUN
cana-5102	225	25	perform	perform	VERB
cana-5102	225	26	well	well	ADV
cana-5102	225	27	on	on	ADP
cana-5102	225	28	both	both	DET
cana-5102	225	29	datasets	dataset	NOUN
cana-5102	225	30	,	,	PUNCT
cana-5102	225	31	but	but	CCONJ
cana-5102	225	32	higher	high	ADJ
cana-5102	225	33	resolution	resolution	NOUN
cana-5102	225	34	(	(	PUNCT
cana-5102	225	35	64x	64x	NOUN
cana-5102	225	36	)	)	PUNCT
cana-5102	225	37	yields	yield	VERB
cana-5102	225	38	better	well	ADJ
cana-5102	225	39	results	result	NOUN
cana-5102	225	40	.	.	PUNCT
cana-5102	226	1	encode	encode	VERB
cana-5102	226	2	rdecode	rdecode	PROPN
cana-5102	226	3	r	r	PROPN
cana-5102	226	4	encode	encode	ADJ
cana-5102	226	5	rdecode	rdecode	NOUN
cana-5102	226	6	r	r	NOUN
cana-5102	226	7	0.9781	0.9781	NUM
cana-5102	226	8	encoderdecoder	encoderdecoder	NOUN
cana-5102	226	9	0.981	0.981	NUM
cana-5102	226	10	encoder	encoder	NOUN
cana-5102	226	11	-	-	PUNCT
cana-5102	226	12	decoder	decoder	NOUN
cana-5102	226	13	achieves	achieve	VERB
cana-5102	226	14	high	high	ADJ
cana-5102	226	15	accuracy	accuracy	NOUN
cana-5102	226	16	on	on	ADP
cana-5102	226	17	both	both	DET
cana-5102	226	18	datasets	dataset	NOUN
cana-5102	226	19	,	,	PUNCT
cana-5102	226	20	with	with	ADP
cana-5102	226	21	slightly	slightly	ADV
cana-5102	226	22	better	well	ADJ
cana-5102	226	23	performance	performance	NOUN
cana-5102	226	24	on	on	ADP
cana-5102	226	25	28x	28x	NOUN
cana-5102	226	26	.	.	PUNCT
cana-5102	227	1	svm	svm	PROPN
cana-5102	227	2	svm	svm	PROPN
cana-5102	227	3	0.9777	0.9777	NUM
cana-5102	227	4	svm	svm	ADJ
cana-5102	227	5	0.725	0.725	NUM
cana-5102	227	6	2	2	NUM
cana-5102	227	7	svm	svm	NOUN
cana-5102	227	8	performs	perform	VERB
cana-5102	227	9	significantly	significantly	ADV
cana-5102	227	10	better	well	ADV
cana-5102	227	11	on	on	ADP
cana-5102	227	12	the	the	DET
cana-5102	227	13	64x	64x	NOUN
cana-5102	227	14	dataset	dataset	NOUN
cana-5102	227	15	compared	compare	VERB
cana-5102	227	16	to	to	ADP
cana-5102	227	17	28x	28x	NOUN
cana-5102	227	18	.	.	PUNCT
cana-5102	228	1	rando	rando	PROPN
cana-5102	228	2	m	m	PROPN
cana-5102	228	3	forest	forest	PROPN
cana-5102	228	4	rando	rando	PROPN
cana-5102	228	5	m	m	PROPN
cana-5102	228	6	forest	forest	NOUN
cana-5102	228	7	0.9538	0.9538	NUM
cana-5102	228	8	random	random	ADJ
cana-5102	228	9	forest	forest	NOUN
cana-5102	228	10	0.953	0.953	NUM
cana-5102	228	11	8	8	NUM
cana-5102	228	12	random	random	ADJ
cana-5102	228	13	forest	forest	NOUN
cana-5102	228	14	shows	show	VERB
cana-5102	228	15	consistent	consistent	ADJ
cana-5102	228	16	performance	performance	NOUN
cana-5102	228	17	across	across	ADP
cana-5102	228	18	both	both	DET
cana-5102	228	19	datasets	dataset	NOUN
cana-5102	228	20	.	.	PUNCT
cana-5102	229	1	lstm	lstm	NOUN
cana-5102	229	2	lstm	lstm	PROPN
cana-5102	229	3	0.0346	0.0346	NUM
cana-5102	229	4	lstm	lstm	NOUN
cana-5102	229	5	performs	perform	VERB
cana-5102	229	6	poorly	poorly	ADV
cana-5102	229	7	on	on	ADP
cana-5102	229	8	the	the	DET
cana-5102	229	9	64x	64x	NOUN
cana-5102	229	10	dataset	dataset	NOUN
cana-5102	229	11	and	and	CCONJ
cana-5102	229	12	is	be	AUX
cana-5102	229	13	not	not	PART
cana-5102	229	14	suitable	suitable	ADJ
cana-5102	229	15	for	for	ADP
cana-5102	229	16	this	this	DET
cana-5102	229	17	task	task	NOUN
cana-5102	229	18	.	.	PUNCT
cana-5102	230	1	communications	communication	NOUN
cana-5102	230	2	on	on	ADP
cana-5102	230	3	applied	apply	VERB
cana-5102	230	4	nonlinear	nonlinear	ADJ
cana-5102	230	5	analysis	analysis	NOUN
cana-5102	230	6	issn	issn	NOUN
cana-5102	230	7	:	:	PUNCT
cana-5102	230	8	1074	1074	NUM
cana-5102	230	9	-	-	PUNCT
cana-5102	230	10	133x	133x	NUM
cana-5102	230	11	vol	vol	NOUN
cana-5102	230	12	32	32	NUM
cana-5102	230	13	no	no	NOUN
cana-5102	230	14	.	.	PUNCT
cana-5102	231	1	icmasd	icmasd	NOUN
cana-5102	231	2	(	(	PUNCT
cana-5102	231	3	2025	2025	NUM
cana-5102	231	4	)	)	PUNCT
cana-5102	232	1	764	764	NUM
cana-5102	232	2	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	232	3	table	table	NOUN
cana-5102	232	4	2	2	NUM
cana-5102	232	5	compares	compare	VERB
cana-5102	232	6	the	the	DET
cana-5102	232	7	performance	performance	NOUN
cana-5102	232	8	of	of	ADP
cana-5102	232	9	various	various	ADJ
cana-5102	232	10	algorithms	algorithm	NOUN
cana-5102	232	11	,	,	PUNCT
cana-5102	232	12	including	include	VERB
cana-5102	232	13	cnn	cnn	PROPN
cana-5102	232	14	,	,	PUNCT
cana-5102	232	15	encoder	encoder	NOUN
cana-5102	232	16	-	-	PUNCT
cana-5102	232	17	decoder	decoder	NOUN
cana-5102	232	18	,	,	PUNCT
cana-5102	232	19	svm	svm	ADJ
cana-5102	232	20	,	,	PUNCT
cana-5102	232	21	random	random	ADJ
cana-5102	232	22	forest	forest	NOUN
cana-5102	232	23	,	,	PUNCT
cana-5102	232	24	and	and	CCONJ
cana-5102	232	25	lstm	lstm	ADJ
cana-5102	232	26	,	,	PUNCT
cana-5102	232	27	on	on	ADP
cana-5102	232	28	two	two	NUM
cana-5102	232	29	datasets	dataset	NOUN
cana-5102	232	30	with	with	ADP
cana-5102	232	31	image	image	NOUN
cana-5102	232	32	resolutions	resolution	NOUN
cana-5102	232	33	of	of	ADP
cana-5102	232	34	64x	64x	NOUN
cana-5102	232	35	and	and	CCONJ
cana-5102	232	36	28x	28x	NOUN
cana-5102	232	37	pixels	pixel	VERB
cana-5102	232	38	.	.	PUNCT
cana-5102	233	1	the	the	DET
cana-5102	233	2	results	result	NOUN
cana-5102	233	3	of	of	ADP
cana-5102	233	4	the	the	DET
cana-5102	233	5	experiment	experiment	NOUN
cana-5102	233	6	demonstrate	demonstrate	VERB
cana-5102	233	7	the	the	DET
cana-5102	233	8	performance	performance	NOUN
cana-5102	233	9	of	of	ADP
cana-5102	233	10	different	different	ADJ
cana-5102	233	11	models	model	NOUN
cana-5102	233	12	on	on	ADP
cana-5102	233	13	these	these	DET
cana-5102	233	14	datasets	dataset	NOUN
cana-5102	233	15	.	.	PUNCT
cana-5102	234	1	cnns	cnns	PROPN
cana-5102	234	2	achieve	achieve	VERB
cana-5102	234	3	the	the	DET
cana-5102	234	4	highest	high	ADJ
cana-5102	234	5	accuracy	accuracy	NOUN
cana-5102	234	6	,	,	PUNCT
cana-5102	234	7	with	with	ADP
cana-5102	234	8	model	model	NOUN
cana-5102	234	9	1	1	NUM
cana-5102	234	10	performing	perform	VERB
cana-5102	234	11	best	good	ADJ
cana-5102	234	12	at	at	ADP
cana-5102	234	13	98.82	98.82	NUM
cana-5102	234	14	%	%	NOUN
cana-5102	234	15	on	on	ADP
cana-5102	234	16	64x	64x	NOUN
cana-5102	234	17	and	and	CCONJ
cana-5102	234	18	model	model	NOUN
cana-5102	234	19	10	10	NUM
cana-5102	234	20	at	at	ADP
cana-5102	234	21	97.63	97.63	NUM
cana-5102	234	22	%	%	NOUN
cana-5102	234	23	on	on	ADP
cana-5102	234	24	28x	28x	NOUN
cana-5102	234	25	.	.	PUNCT
cana-5102	235	1	encoder	encoder	NOUN
cana-5102	235	2	-	-	PUNCT
cana-5102	235	3	decoder	decoder	NOUN
cana-5102	235	4	models	model	NOUN
cana-5102	235	5	also	also	ADV
cana-5102	235	6	perform	perform	VERB
cana-5102	235	7	well	well	ADV
cana-5102	235	8	,	,	PUNCT
cana-5102	235	9	showing	show	VERB
cana-5102	235	10	a	a	DET
cana-5102	235	11	slight	slight	ADJ
cana-5102	235	12	improvement	improvement	NOUN
cana-5102	235	13	on	on	ADP
cana-5102	235	14	28x	28x	NOUN
cana-5102	235	15	.	.	PUNCT
cana-5102	236	1	svm	svm	PROPN
cana-5102	236	2	shows	show	VERB
cana-5102	236	3	a	a	DET
cana-5102	236	4	significant	significant	ADJ
cana-5102	236	5	drop	drop	NOUN
cana-5102	236	6	in	in	ADP
cana-5102	236	7	accuracy	accuracy	NOUN
cana-5102	236	8	from	from	ADP
cana-5102	236	9	97.77	97.77	NUM
cana-5102	236	10	%	%	NOUN
cana-5102	236	11	on	on	ADP
cana-5102	236	12	64x	64x	NOUN
cana-5102	236	13	to	to	ADP
cana-5102	236	14	72.52	72.52	NUM
cana-5102	236	15	%	%	NOUN
cana-5102	236	16	on	on	ADP
cana-5102	236	17	28x	28x	NOUN
cana-5102	236	18	,	,	PUNCT
cana-5102	236	19	indicating	indicate	VERB
cana-5102	236	20	its	its	PRON
cana-5102	236	21	sensitivity	sensitivity	NOUN
cana-5102	236	22	to	to	PART
cana-5102	236	23	resolution	resolution	VERB
cana-5102	236	24	.	.	PUNCT
cana-5102	237	1	random	random	ADJ
cana-5102	237	2	forest	forest	NOUN
cana-5102	237	3	maintains	maintain	VERB
cana-5102	237	4	stable	stable	ADJ
cana-5102	237	5	accuracy	accuracy	NOUN
cana-5102	237	6	across	across	ADP
cana-5102	237	7	both	both	DET
cana-5102	237	8	datasets	dataset	NOUN
cana-5102	237	9	at	at	ADP
cana-5102	237	10	95.38	95.38	NUM
cana-5102	237	11	%	%	NOUN
cana-5102	237	12	.	.	PUNCT
cana-5102	238	1	lstm	lstm	NOUN
cana-5102	238	2	performs	perform	VERB
cana-5102	238	3	poorly	poorly	ADV
cana-5102	238	4	,	,	PUNCT
cana-5102	238	5	achieving	achieve	VERB
cana-5102	238	6	only	only	ADV
cana-5102	238	7	3.46	3.46	NUM
cana-5102	238	8	%	%	NOUN
cana-5102	238	9	accuracy	accuracy	NOUN
cana-5102	238	10	on	on	ADP
cana-5102	238	11	64x	64x	NOUN
cana-5102	238	12	,	,	PUNCT
cana-5102	238	13	making	make	VERB
cana-5102	238	14	it	it	PRON
cana-5102	238	15	unsuitable	unsuitable	ADJ
cana-5102	238	16	for	for	ADP
cana-5102	238	17	this	this	DET
cana-5102	238	18	task	task	NOUN
cana-5102	238	19	.	.	PUNCT
cana-5102	239	1	table	table	NOUN
cana-5102	239	2	3	3	NUM
cana-5102	239	3	:	:	PUNCT
cana-5102	239	4	selected	select	VERB
cana-5102	239	5	cnn	cnn	PROPN
cana-5102	239	6	models	model	NOUN
cana-5102	239	7	based	base	VERB
cana-5102	239	8	on	on	ADP
cana-5102	239	9	different	different	ADJ
cana-5102	239	10	parameters	parameter	NOUN
cana-5102	239	11	model	model	NOUN
cana-5102	239	12	filters	filter	NOUN
cana-5102	239	13	conv	conv	ADJ
cana-5102	239	14	layers	layer	NOUN
cana-5102	239	15	activation	activation	VERB
cana-5102	239	16	dense	dense	ADJ
cana-5102	239	17	units	unit	NOUN
cana-5102	239	18	dropout	dropout	NOUN
cana-5102	239	19	rate	rate	NOUN
cana-5102	239	20	optimizer	optimizer	NOUN
cana-5102	239	21	model	model	NOUN
cana-5102	239	22	1	1	NUM
cana-5102	239	23	128	128	NUM
cana-5102	239	24	4	4	NUM
cana-5102	239	25	relu	relu	NOUN
cana-5102	239	26	128	128	NUM
cana-5102	239	27	0.3	0.3	NUM
cana-5102	239	28	rmsprop	rmsprop	NOUN
cana-5102	239	29	model	model	NOUN
cana-5102	239	30	2	2	NUM
cana-5102	239	31	32	32	NUM
cana-5102	239	32	3	3	NUM
cana-5102	239	33	relu	relu	NOUN
cana-5102	239	34	512	512	NUM
cana-5102	239	35	0.3	0.3	NUM
cana-5102	239	36	adam	adam	PROPN
cana-5102	239	37	model	model	NOUN
cana-5102	239	38	3	3	NUM
cana-5102	239	39	128	128	NUM
cana-5102	239	40	2	2	NUM
cana-5102	239	41	relu	relu	NOUN
cana-5102	239	42	512	512	NUM
cana-5102	239	43	0.2	0.2	NUM
cana-5102	239	44	adam	adam	PROPN
cana-5102	239	45	model	model	NOUN
cana-5102	239	46	4	4	NUM
cana-5102	239	47	64	64	NUM
cana-5102	239	48	4	4	NUM
cana-5102	239	49	sigmoid	sigmoid	NOUN
cana-5102	239	50	512	512	NUM
cana-5102	239	51	0.3	0.3	NUM
cana-5102	239	52	adam	adam	PROPN
cana-5102	239	53	model	model	NOUN
cana-5102	239	54	5	5	NUM
cana-5102	239	55	128	128	NUM
cana-5102	239	56	3	3	NUM
cana-5102	239	57	tanh	tanh	PROPN
cana-5102	239	58	512	512	NUM
cana-5102	239	59	0.5	0.5	NUM
cana-5102	239	60	sgd	sgd	NOUN
cana-5102	239	61	model	model	NOUN
cana-5102	239	62	6	6	NUM
cana-5102	239	63	64	64	NUM
cana-5102	239	64	3	3	NUM
cana-5102	239	65	sigmoid	sigmoid	NOUN
cana-5102	239	66	256	256	NUM
cana-5102	239	67	0.5	0.5	NUM
cana-5102	239	68	adam	adam	PROPN
cana-5102	239	69	model	model	NOUN
cana-5102	239	70	7	7	NUM
cana-5102	239	71	64	64	NUM
cana-5102	239	72	2	2	NUM
cana-5102	239	73	sigmoid	sigmoid	NOUN
cana-5102	239	74	512	512	NUM
cana-5102	239	75	0.3	0.3	NUM
cana-5102	239	76	rmsprop	rmsprop	NOUN
cana-5102	239	77	model	model	NOUN
cana-5102	239	78	8	8	NUM
cana-5102	239	79	64	64	NUM
cana-5102	239	80	3	3	NUM
cana-5102	239	81	sigmoid	sigmoid	NOUN
cana-5102	239	82	256	256	NUM
cana-5102	239	83	0.3	0.3	NUM
cana-5102	239	84	sgd	sgd	NOUN
cana-5102	239	85	model	model	NOUN
cana-5102	239	86	9	9	NUM
cana-5102	239	87	64	64	NUM
cana-5102	239	88	4	4	NUM
cana-5102	239	89	sigmoid	sigmoid	NOUN
cana-5102	239	90	512	512	NUM
cana-5102	239	91	0.5	0.5	NUM
cana-5102	239	92	rmsprop	rmsprop	NOUN
cana-5102	239	93	model	model	NOUN
cana-5102	239	94	10	10	NUM
cana-5102	239	95	64	64	NUM
cana-5102	239	96	2	2	NUM
cana-5102	239	97	sigmoid	sigmoid	NOUN
cana-5102	239	98	256	256	NUM
cana-5102	239	99	0.2	0.2	NUM
cana-5102	239	100	sgd	sgd	NOUN
cana-5102	239	101	table	table	NOUN
cana-5102	239	102	3	3	NUM
cana-5102	239	103	shows	show	VERB
cana-5102	239	104	different	different	ADJ
cana-5102	239	105	cnn	cnn	PROPN
cana-5102	239	106	models	model	NOUN
cana-5102	239	107	selected	select	VERB
cana-5102	239	108	based	base	VERB
cana-5102	239	109	on	on	ADP
cana-5102	239	110	filters	filter	NOUN
cana-5102	239	111	,	,	PUNCT
cana-5102	239	112	convolutional	convolutional	ADJ
cana-5102	239	113	layers	layer	NOUN
cana-5102	239	114	,	,	PUNCT
cana-5102	239	115	activation	activation	NOUN
cana-5102	239	116	function	function	NOUN
cana-5102	239	117	,	,	PUNCT
cana-5102	239	118	dense	dense	ADJ
cana-5102	239	119	units	unit	NOUN
cana-5102	239	120	,	,	PUNCT
cana-5102	239	121	dropout	dropout	NOUN
cana-5102	239	122	rates	rate	NOUN
cana-5102	239	123	and	and	CCONJ
cana-5102	239	124	optimizers	optimizer	NOUN
cana-5102	239	125	.	.	PUNCT
cana-5102	240	1	this	this	DET
cana-5102	240	2	study	study	NOUN
cana-5102	240	3	investigates	investigate	VERB
cana-5102	240	4	the	the	DET
cana-5102	240	5	performance	performance	NOUN
cana-5102	240	6	of	of	ADP
cana-5102	240	7	10	10	NUM
cana-5102	240	8	commonly	commonly	ADV
cana-5102	240	9	used	use	VERB
cana-5102	240	10	cnn	cnn	PROPN
cana-5102	240	11	models	model	NOUN
cana-5102	240	12	with	with	ADP
cana-5102	240	13	varying	vary	VERB
cana-5102	240	14	architectures	architecture	NOUN
cana-5102	240	15	,	,	PUNCT
cana-5102	240	16	including	include	VERB
cana-5102	240	17	different	different	ADJ
cana-5102	240	18	filter	filter	NOUN
cana-5102	240	19	sizes	size	NOUN
cana-5102	240	20	,	,	PUNCT
cana-5102	240	21	convolutional	convolutional	ADJ
cana-5102	240	22	communications	communication	NOUN
cana-5102	240	23	on	on	ADP
cana-5102	240	24	applied	apply	VERB
cana-5102	240	25	nonlinear	nonlinear	ADJ
cana-5102	240	26	analysis	analysis	NOUN
cana-5102	240	27	issn	issn	NOUN
cana-5102	240	28	:	:	PUNCT
cana-5102	240	29	1074	1074	NUM
cana-5102	240	30	-	-	PUNCT
cana-5102	240	31	133x	133x	NUM
cana-5102	240	32	vol	vol	NOUN
cana-5102	240	33	32	32	NUM
cana-5102	240	34	no	no	NOUN
cana-5102	240	35	.	.	PUNCT
cana-5102	241	1	icmasd	icmasd	NOUN
cana-5102	241	2	(	(	PUNCT
cana-5102	241	3	2025	2025	NUM
cana-5102	241	4	)	)	PUNCT
cana-5102	241	5	765	765	NUM
cana-5102	241	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	241	7	layers	layer	NOUN
cana-5102	241	8	,	,	PUNCT
cana-5102	241	9	activation	activation	NOUN
cana-5102	241	10	functions	function	NOUN
cana-5102	241	11	,	,	PUNCT
cana-5102	241	12	dense	dense	ADJ
cana-5102	241	13	units	unit	NOUN
cana-5102	241	14	,	,	PUNCT
cana-5102	241	15	dropout	dropout	NOUN
cana-5102	241	16	rates	rate	NOUN
cana-5102	241	17	,	,	PUNCT
cana-5102	241	18	and	and	CCONJ
cana-5102	241	19	optimizers	optimizer	NOUN
cana-5102	241	20	.	.	PUNCT
cana-5102	242	1	these	these	DET
cana-5102	242	2	models	model	NOUN
cana-5102	242	3	were	be	AUX
cana-5102	242	4	trained	train	VERB
cana-5102	242	5	and	and	CCONJ
cana-5102	242	6	evaluated	evaluate	VERB
cana-5102	242	7	on	on	ADP
cana-5102	242	8	two	two	NUM
cana-5102	242	9	datasets	dataset	NOUN
cana-5102	242	10	with	with	ADP
cana-5102	242	11	resolutions	resolution	NOUN
cana-5102	242	12	of	of	ADP
cana-5102	242	13	64x	64x	NOUN
cana-5102	242	14	and	and	CCONJ
cana-5102	242	15	28x	28x	NOUN
cana-5102	242	16	to	to	PART
cana-5102	242	17	compare	compare	VERB
cana-5102	242	18	their	their	PRON
cana-5102	242	19	effectiveness	effectiveness	NOUN
cana-5102	242	20	.	.	PUNCT
cana-5102	243	1	fig	fig	NOUN
cana-5102	243	2	2	2	NUM
cana-5102	243	3	and	and	CCONJ
cana-5102	243	4	fig	fig	NOUN
cana-5102	243	5	3	3	NUM
cana-5102	243	6	compares	compare	VERB
cana-5102	243	7	the	the	DET
cana-5102	243	8	results	result	NOUN
cana-5102	243	9	of	of	ADP
cana-5102	243	10	accuracy	accuracy	NOUN
cana-5102	243	11	and	and	CCONJ
cana-5102	243	12	loss	loss	NOUN
cana-5102	243	13	of	of	ADP
cana-5102	243	14	best	good	ADJ
cana-5102	243	15	performing	performing	NOUN
cana-5102	243	16	models	model	NOUN
cana-5102	243	17	respectively	respectively	ADV
cana-5102	243	18	.	.	PUNCT
cana-5102	244	1	both	both	DET
cana-5102	244	2	the	the	DET
cana-5102	244	3	figures	figure	NOUN
cana-5102	244	4	are	be	AUX
cana-5102	244	5	results	result	NOUN
cana-5102	244	6	after	after	ADP
cana-5102	244	7	training	train	VERB
cana-5102	244	8	the	the	DET
cana-5102	244	9	model	model	NOUN
cana-5102	244	10	with	with	ADP
cana-5102	244	11	28x	28x	NOUN
cana-5102	244	12	bit	bit	NOUN
cana-5102	244	13	images	image	NOUN
cana-5102	244	14	dataset	dataset	VERB
cana-5102	244	15	and	and	CCONJ
cana-5102	244	16	64x	64x	NUM
cana-5102	244	17	images	image	NOUN
cana-5102	244	18	database	database	NOUN
cana-5102	244	19	respectively	respectively	ADV
cana-5102	244	20	.	.	PUNCT
cana-5102	245	1	the	the	DET
cana-5102	245	2	results	result	NOUN
cana-5102	245	3	indicate	indicate	VERB
cana-5102	245	4	that	that	DET
cana-5102	245	5	model	model	NOUN
cana-5102	245	6	3	3	NUM
cana-5102	245	7	achieved	achieve	VERB
cana-5102	245	8	the	the	DET
cana-5102	245	9	highest	high	ADJ
cana-5102	245	10	accuracy	accuracy	NOUN
cana-5102	245	11	of	of	ADP
cana-5102	245	12	99.07	99.07	NUM
cana-5102	245	13	%	%	NOUN
cana-5102	245	14	on	on	ADP
cana-5102	245	15	both	both	DET
cana-5102	245	16	datasets	dataset	NOUN
cana-5102	245	17	,	,	PUNCT
cana-5102	245	18	demonstrating	demonstrate	VERB
cana-5102	245	19	strong	strong	ADJ
cana-5102	245	20	generalization	generalization	NOUN
cana-5102	245	21	across	across	ADP
cana-5102	245	22	different	different	ADJ
cana-5102	245	23	resolutions	resolution	NOUN
cana-5102	245	24	.	.	PUNCT
cana-5102	246	1	model	model	NOUN
cana-5102	246	2	2	2	NUM
cana-5102	246	3	also	also	ADV
cana-5102	246	4	performed	perform	VERB
cana-5102	246	5	well	well	ADV
cana-5102	246	6	,	,	PUNCT
cana-5102	246	7	achieving	achieve	VERB
cana-5102	246	8	over	over	ADP
cana-5102	246	9	97	97	NUM
cana-5102	246	10	%	%	NOUN
cana-5102	246	11	accuracy	accuracy	NOUN
cana-5102	246	12	on	on	ADP
cana-5102	246	13	both	both	DET
cana-5102	246	14	datasets	dataset	NOUN
cana-5102	246	15	.	.	PUNCT
cana-5102	247	1	in	in	ADP
cana-5102	247	2	contrast	contrast	NOUN
cana-5102	247	3	,	,	PUNCT
cana-5102	247	4	models	model	NOUN
cana-5102	247	5	8	8	NUM
cana-5102	247	6	,	,	PUNCT
cana-5102	247	7	9	9	NUM
cana-5102	247	8	,	,	PUNCT
cana-5102	247	9	and	and	CCONJ
cana-5102	247	10	10	10	NUM
cana-5102	247	11	exhibited	exhibit	VERB
cana-5102	247	12	significantly	significantly	ADV
cana-5102	247	13	lower	low	ADJ
cana-5102	247	14	performance	performance	NOUN
cana-5102	247	15	,	,	PUNCT
cana-5102	247	16	with	with	ADP
cana-5102	247	17	accuracies	accuracy	NOUN
cana-5102	247	18	around	around	ADP
cana-5102	247	19	3.5	3.5	NUM
cana-5102	247	20	%	%	NOUN
cana-5102	247	21	,	,	PUNCT
cana-5102	247	22	suggesting	suggest	VERB
cana-5102	247	23	that	that	SCONJ
cana-5102	247	24	their	their	PRON
cana-5102	247	25	configurations	configuration	NOUN
cana-5102	247	26	were	be	AUX
cana-5102	247	27	ineffective	ineffective	ADJ
cana-5102	247	28	for	for	ADP
cana-5102	247	29	the	the	DET
cana-5102	247	30	ocr	ocr	PROPN
cana-5102	247	31	task	task	NOUN
cana-5102	247	32	.	.	PUNCT
cana-5102	248	1	the	the	DET
cana-5102	248	2	findings	finding	NOUN
cana-5102	248	3	highlight	highlight	VERB
cana-5102	248	4	that	that	SCONJ
cana-5102	248	5	models	model	NOUN
cana-5102	248	6	with	with	ADP
cana-5102	248	7	optimized	optimize	VERB
cana-5102	248	8	convolutional	convolutional	ADJ
cana-5102	248	9	layers	layer	NOUN
cana-5102	248	10	,	,	PUNCT
cana-5102	248	11	suitable	suitable	ADJ
cana-5102	248	12	activation	activation	NOUN
cana-5102	248	13	functions	function	NOUN
cana-5102	248	14	,	,	PUNCT
cana-5102	248	15	and	and	CCONJ
cana-5102	248	16	well	well	ADV
cana-5102	248	17	-	-	PUNCT
cana-5102	248	18	tuned	tune	VERB
cana-5102	248	19	dropout	dropout	NOUN
cana-5102	248	20	rates	rate	NOUN
cana-5102	248	21	tend	tend	VERB
cana-5102	248	22	to	to	PART
cana-5102	248	23	yield	yield	VERB
cana-5102	248	24	superior	superior	ADJ
cana-5102	248	25	results	result	NOUN
cana-5102	248	26	.	.	PUNCT
cana-5102	249	1	fig	fig	NOUN
cana-5102	249	2	2	2	NUM
cana-5102	249	3	:	:	PUNCT
cana-5102	249	4	comparison	comparison	NOUN
cana-5102	249	5	of	of	ADP
cana-5102	249	6	accuracy	accuracy	NOUN
cana-5102	249	7	of	of	ADP
cana-5102	249	8	best	good	ADJ
cana-5102	249	9	performing	performing	NOUN
cana-5102	249	10	models	model	NOUN
cana-5102	249	11	fig	fig	VERB
cana-5102	249	12	3	3	NUM
cana-5102	249	13	:	:	PUNCT
cana-5102	249	14	comparison	comparison	NOUN
cana-5102	249	15	of	of	ADP
cana-5102	249	16	loss	loss	NOUN
cana-5102	249	17	of	of	ADP
cana-5102	249	18	best	good	ADJ
cana-5102	249	19	performing	performing	NOUN
cana-5102	249	20	models	model	NOUN
cana-5102	249	21	communications	communication	NOUN
cana-5102	249	22	on	on	ADP
cana-5102	249	23	applied	apply	VERB
cana-5102	249	24	nonlinear	nonlinear	ADJ
cana-5102	249	25	analysis	analysis	NOUN
cana-5102	249	26	issn	issn	NOUN
cana-5102	249	27	:	:	PUNCT
cana-5102	249	28	1074	1074	NUM
cana-5102	249	29	-	-	PUNCT
cana-5102	249	30	133x	133x	NUM
cana-5102	249	31	vol	vol	NOUN
cana-5102	249	32	32	32	NUM
cana-5102	249	33	no	no	NOUN
cana-5102	249	34	.	.	PUNCT
cana-5102	250	1	icmasd	icmasd	NOUN
cana-5102	250	2	(	(	PUNCT
cana-5102	250	3	2025	2025	NUM
cana-5102	250	4	)	)	PUNCT
cana-5102	250	5	766	766	NUM
cana-5102	250	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	250	7	the	the	DET
cana-5102	250	8	training	training	NOUN
cana-5102	250	9	history	history	NOUN
cana-5102	250	10	of	of	ADP
cana-5102	250	11	the	the	DET
cana-5102	250	12	models	model	NOUN
cana-5102	250	13	across	across	ADP
cana-5102	250	14	both	both	CCONJ
cana-5102	250	15	the	the	DET
cana-5102	250	16	64x	64x	NOUN
cana-5102	250	17	and	and	CCONJ
cana-5102	250	18	28x	28x	NOUN
cana-5102	250	19	datasets	dataset	NOUN
cana-5102	250	20	provides	provide	VERB
cana-5102	250	21	several	several	ADJ
cana-5102	250	22	significant	significant	ADJ
cana-5102	250	23	insights	insight	NOUN
cana-5102	250	24	.	.	PUNCT
cana-5102	251	1	models	model	NOUN
cana-5102	251	2	developed	develop	VERB
cana-5102	251	3	using	use	VERB
cana-5102	251	4	the	the	DET
cana-5102	251	5	64x	64x	NOUN
cana-5102	251	6	dataset	dataset	NOUN
cana-5102	251	7	typically	typically	ADV
cana-5102	251	8	demonstrate	demonstrate	VERB
cana-5102	251	9	superior	superior	ADJ
cana-5102	251	10	accuracy	accuracy	NOUN
cana-5102	251	11	and	and	CCONJ
cana-5102	251	12	reduced	reduce	VERB
cana-5102	251	13	loss	loss	NOUN
cana-5102	251	14	in	in	ADP
cana-5102	251	15	comparison	comparison	NOUN
cana-5102	251	16	to	to	ADP
cana-5102	251	17	those	those	PRON
cana-5102	251	18	utilizing	utilize	VERB
cana-5102	251	19	the	the	DET
cana-5102	251	20	28x	28x	NOUN
cana-5102	251	21	dataset	dataset	VERB
cana-5102	251	22	.	.	PUNCT
cana-5102	252	1	for	for	ADP
cana-5102	252	2	instance	instance	NOUN
cana-5102	252	3	,	,	PUNCT
cana-5102	252	4	model	model	NOUN
cana-5102	252	5	3	3	NUM
cana-5102	252	6	achieves	achieve	VERB
cana-5102	252	7	a	a	DET
cana-5102	252	8	final	final	ADJ
cana-5102	252	9	accuracy	accuracy	NOUN
cana-5102	252	10	of	of	ADP
cana-5102	252	11	0.9907	0.9907	NUM
cana-5102	252	12	with	with	ADP
cana-5102	252	13	a	a	DET
cana-5102	252	14	loss	loss	NOUN
cana-5102	252	15	of	of	ADP
cana-5102	252	16	0.0287	0.0287	NUM
cana-5102	252	17	when	when	SCONJ
cana-5102	252	18	trained	train	VERB
cana-5102	252	19	on	on	ADP
cana-5102	252	20	the	the	DET
cana-5102	252	21	64x	64x	NOUN
cana-5102	252	22	dataset	dataset	NOUN
cana-5102	252	23	,	,	PUNCT
cana-5102	252	24	underscoring	underscore	VERB
cana-5102	252	25	the	the	DET
cana-5102	252	26	benefits	benefit	NOUN
cana-5102	252	27	of	of	ADP
cana-5102	252	28	higherresolution	higherresolution	NOUN
cana-5102	252	29	images	image	NOUN
cana-5102	252	30	for	for	ADP
cana-5102	252	31	effective	effective	ADJ
cana-5102	252	32	feature	feature	NOUN
cana-5102	252	33	extraction	extraction	NOUN
cana-5102	252	34	.	.	PUNCT
cana-5102	253	1	the	the	DET
cana-5102	253	2	trends	trend	NOUN
cana-5102	253	3	in	in	ADP
cana-5102	253	4	validation	validation	NOUN
cana-5102	253	5	accuracy	accuracy	NOUN
cana-5102	253	6	and	and	CCONJ
cana-5102	253	7	loss	loss	NOUN
cana-5102	253	8	mirror	mirror	NOUN
cana-5102	253	9	these	these	DET
cana-5102	253	10	findings	finding	NOUN
cana-5102	253	11	,	,	PUNCT
cana-5102	253	12	with	with	ADP
cana-5102	253	13	models	model	NOUN
cana-5102	253	14	such	such	ADJ
cana-5102	253	15	as	as	ADP
cana-5102	253	16	model	model	NOUN
cana-5102	253	17	3	3	NUM
cana-5102	253	18	and	and	CCONJ
cana-5102	253	19	model	model	NOUN
cana-5102	253	20	2	2	NUM
cana-5102	253	21	exhibiting	exhibit	VERB
cana-5102	253	22	robust	robust	ADJ
cana-5102	253	23	generalization	generalization	NOUN
cana-5102	253	24	capabilities	capability	NOUN
cana-5102	253	25	on	on	ADP
cana-5102	253	26	the	the	DET
cana-5102	253	27	64x	64x	NOUN
cana-5102	253	28	dataset	dataset	NOUN
cana-5102	253	29	.	.	PUNCT
cana-5102	254	1	conversely	conversely	ADV
cana-5102	254	2	,	,	PUNCT
cana-5102	254	3	models	model	NOUN
cana-5102	254	4	trained	train	VERB
cana-5102	254	5	on	on	ADP
cana-5102	254	6	the	the	DET
cana-5102	254	7	28x	28x	NOUN
cana-5102	254	8	dataset	dataset	NOUN
cana-5102	254	9	show	show	VERB
cana-5102	254	10	a	a	DET
cana-5102	254	11	slight	slight	ADJ
cana-5102	254	12	decline	decline	NOUN
cana-5102	254	13	in	in	ADP
cana-5102	254	14	accuracy	accuracy	NOUN
cana-5102	254	15	and	and	CCONJ
cana-5102	254	16	an	an	DET
cana-5102	254	17	increase	increase	NOUN
cana-5102	254	18	in	in	ADP
cana-5102	254	19	loss	loss	NOUN
cana-5102	254	20	.	.	PUNCT
cana-5102	255	1	for	for	ADP
cana-5102	255	2	example	example	NOUN
cana-5102	255	3	,	,	PUNCT
cana-5102	255	4	model	model	NOUN
cana-5102	255	5	3	3	NUM
cana-5102	255	6	records	record	NOUN
cana-5102	255	7	a	a	DET
cana-5102	255	8	final	final	ADJ
cana-5102	255	9	accuracy	accuracy	NOUN
cana-5102	255	10	of	of	ADP
cana-5102	255	11	0.9907	0.9907	NUM
cana-5102	255	12	and	and	CCONJ
cana-5102	255	13	a	a	DET
cana-5102	255	14	loss	loss	NOUN
cana-5102	255	15	of	of	ADP
cana-5102	255	16	0.0291	0.0291	NUM
cana-5102	255	17	on	on	ADP
cana-5102	255	18	the	the	DET
cana-5102	255	19	28x	28x	NOUN
cana-5102	255	20	dataset	dataset	PROPN
cana-5102	255	21	,	,	PUNCT
cana-5102	255	22	which	which	PRON
cana-5102	255	23	,	,	PUNCT
cana-5102	255	24	although	although	SCONJ
cana-5102	255	25	commendable	commendable	ADJ
cana-5102	255	26	,	,	PUNCT
cana-5102	255	27	is	be	AUX
cana-5102	255	28	marginally	marginally	ADV
cana-5102	255	29	inferior	inferior	ADJ
cana-5102	255	30	to	to	ADP
cana-5102	255	31	its	its	PRON
cana-5102	255	32	performance	performance	NOUN
cana-5102	255	33	on	on	ADP
cana-5102	255	34	the	the	DET
cana-5102	255	35	64x	64x	NOUN
cana-5102	255	36	dataset	dataset	NOUN
cana-5102	255	37	.	.	PUNCT
cana-5102	256	1	the	the	DET
cana-5102	256	2	validation	validation	NOUN
cana-5102	256	3	metrics	metric	NOUN
cana-5102	256	4	for	for	ADP
cana-5102	256	5	the	the	DET
cana-5102	256	6	28x	28x	NOUN
cana-5102	256	7	dataset	dataset	VERB
cana-5102	256	8	also	also	ADV
cana-5102	256	9	reflect	reflect	VERB
cana-5102	256	10	a	a	DET
cana-5102	256	11	somewhat	somewhat	ADV
cana-5102	256	12	diminished	diminished	ADJ
cana-5102	256	13	generalization	generalization	NOUN
cana-5102	256	14	ability	ability	NOUN
cana-5102	256	15	,	,	PUNCT
cana-5102	256	16	indicating	indicate	VERB
cana-5102	256	17	that	that	SCONJ
cana-5102	256	18	lower	low	ADJ
cana-5102	256	19	-	-	PUNCT
cana-5102	256	20	resolution	resolution	NOUN
cana-5102	256	21	images	image	NOUN
cana-5102	256	22	may	may	AUX
cana-5102	256	23	hinder	hinder	VERB
cana-5102	256	24	the	the	DET
cana-5102	256	25	model	model	NOUN
cana-5102	256	26	's	's	PART
cana-5102	256	27	capacity	capacity	NOUN
cana-5102	256	28	to	to	PART
cana-5102	256	29	learn	learn	VERB
cana-5102	256	30	complex	complex	ADJ
cana-5102	256	31	patterns	pattern	NOUN
cana-5102	256	32	effectively	effectively	ADV
cana-5102	256	33	.	.	PUNCT
cana-5102	257	1	in	in	ADP
cana-5102	257	2	the	the	DET
cana-5102	257	3	comparative	comparative	ADJ
cana-5102	257	4	analysis	analysis	NOUN
cana-5102	257	5	of	of	ADP
cana-5102	257	6	different	different	ADJ
cana-5102	257	7	models	model	NOUN
cana-5102	257	8	,	,	PUNCT
cana-5102	257	9	model	model	NOUN
cana-5102	257	10	3	3	NUM
cana-5102	257	11	consistently	consistently	ADV
cana-5102	257	12	excels	excel	VERB
cana-5102	257	13	across	across	ADP
cana-5102	257	14	both	both	DET
cana-5102	257	15	datasets	dataset	NOUN
cana-5102	257	16	,	,	PUNCT
cana-5102	257	17	achieving	achieve	VERB
cana-5102	257	18	the	the	DET
cana-5102	257	19	highest	high	ADJ
cana-5102	257	20	accuracy	accuracy	NOUN
cana-5102	257	21	and	and	CCONJ
cana-5102	257	22	the	the	DET
cana-5102	257	23	lowest	low	ADJ
cana-5102	257	24	loss	loss	NOUN
cana-5102	257	25	.	.	PUNCT
cana-5102	258	1	this	this	DET
cana-5102	258	2	performance	performance	NOUN
cana-5102	258	3	suggests	suggest	VERB
cana-5102	258	4	that	that	SCONJ
cana-5102	258	5	its	its	PRON
cana-5102	258	6	architectural	architectural	ADJ
cana-5102	258	7	design	design	NOUN
cana-5102	258	8	,	,	PUNCT
cana-5102	258	9	which	which	PRON
cana-5102	258	10	includes	include	VERB
cana-5102	258	11	considerations	consideration	NOUN
cana-5102	258	12	such	such	ADJ
cana-5102	258	13	as	as	ADP
cana-5102	258	14	the	the	DET
cana-5102	258	15	number	number	NOUN
cana-5102	258	16	of	of	ADP
cana-5102	258	17	filters	filter	NOUN
cana-5102	258	18	,	,	PUNCT
cana-5102	258	19	layers	layer	NOUN
cana-5102	258	20	,	,	PUNCT
cana-5102	258	21	and	and	CCONJ
cana-5102	258	22	activation	activation	NOUN
cana-5102	258	23	functions	function	NOUN
cana-5102	258	24	,	,	PUNCT
cana-5102	258	25	is	be	AUX
cana-5102	258	26	particularly	particularly	ADV
cana-5102	258	27	well	well	ADV
cana-5102	258	28	-	-	PUNCT
cana-5102	258	29	suited	suited	ADJ
cana-5102	258	30	for	for	ADP
cana-5102	258	31	the	the	DET
cana-5102	258	32	ocr	ocr	PROPN
cana-5102	258	33	task	task	NOUN
cana-5102	258	34	.	.	PUNCT
cana-5102	259	1	in	in	ADP
cana-5102	259	2	contrast	contrast	NOUN
cana-5102	259	3	,	,	PUNCT
cana-5102	259	4	models	model	NOUN
cana-5102	259	5	such	such	ADJ
cana-5102	259	6	as	as	ADP
cana-5102	259	7	model	model	NOUN
cana-5102	259	8	8	8	NUM
cana-5102	259	9	,	,	PUNCT
cana-5102	259	10	model	model	NOUN
cana-5102	259	11	9	9	NUM
cana-5102	259	12	,	,	PUNCT
cana-5102	259	13	and	and	CCONJ
cana-5102	259	14	model	model	NOUN
cana-5102	259	15	10	10	NUM
cana-5102	259	16	exhibit	exhibit	NOUN
cana-5102	259	17	poor	poor	ADJ
cana-5102	259	18	performance	performance	NOUN
cana-5102	259	19	across	across	ADP
cana-5102	259	20	both	both	DET
cana-5102	259	21	datasets	dataset	NOUN
cana-5102	259	22	,	,	PUNCT
cana-5102	259	23	with	with	ADP
cana-5102	259	24	final	final	ADJ
cana-5102	259	25	accuracies	accuracy	NOUN
cana-5102	259	26	falling	fall	VERB
cana-5102	259	27	below	below	ADP
cana-5102	259	28	0.05	0.05	NUM
cana-5102	259	29	and	and	CCONJ
cana-5102	259	30	high	high	ADJ
cana-5102	259	31	loss	loss	NOUN
cana-5102	259	32	values	value	NOUN
cana-5102	259	33	,	,	PUNCT
cana-5102	259	34	indicating	indicate	VERB
cana-5102	259	35	potential	potential	ADJ
cana-5102	259	36	inadequacies	inadequacy	NOUN
cana-5102	259	37	in	in	ADP
cana-5102	259	38	their	their	PRON
cana-5102	259	39	architectures	architecture	NOUN
cana-5102	259	40	or	or	CCONJ
cana-5102	259	41	hyperparameter	hyperparameter	NOUN
cana-5102	259	42	settings	setting	NOUN
cana-5102	259	43	.	.	PUNCT
cana-5102	260	1	a	a	DET
cana-5102	260	2	notable	notable	ADJ
cana-5102	260	3	trend	trend	NOUN
cana-5102	260	4	in	in	ADP
cana-5102	260	5	the	the	DET
cana-5102	260	6	training	training	NOUN
cana-5102	260	7	history	history	NOUN
cana-5102	260	8	shown	show	VERB
cana-5102	260	9	in	in	ADP
cana-5102	260	10	figure	figure	NOUN
cana-5102	260	11	4	4	NUM
cana-5102	260	12	and	and	CCONJ
cana-5102	260	13	figure	figure	VERB
cana-5102	260	14	5	5	NUM
cana-5102	260	15	indicates	indicate	VERB
cana-5102	260	16	that	that	SCONJ
cana-5102	260	17	models	model	NOUN
cana-5102	260	18	employing	employ	VERB
cana-5102	260	19	relu	relu	NOUN
cana-5102	260	20	activation	activation	NOUN
cana-5102	260	21	functions	function	NOUN
cana-5102	260	22	and	and	CCONJ
cana-5102	260	23	optimizers	optimizer	NOUN
cana-5102	260	24	like	like	ADP
cana-5102	260	25	adam	adam	NOUN
cana-5102	260	26	or	or	CCONJ
cana-5102	260	27	rmsprop	rmsprop	NOUN
cana-5102	260	28	tend	tend	VERB
cana-5102	260	29	to	to	PART
cana-5102	260	30	outperform	outperform	VERB
cana-5102	260	31	those	those	PRON
cana-5102	260	32	that	that	PRON
cana-5102	260	33	utilize	utilize	VERB
cana-5102	260	34	sigmoid	sigmoid	NOUN
cana-5102	260	35	activation	activation	NOUN
cana-5102	260	36	and	and	CCONJ
cana-5102	260	37	sgd	sgd	NOUN
cana-5102	260	38	optimizers	optimizer	NOUN
cana-5102	260	39	.	.	PUNCT
cana-5102	261	1	while	while	SCONJ
cana-5102	261	2	higher	high	ADJ
cana-5102	261	3	-	-	PUNCT
cana-5102	261	4	resolution	resolution	NOUN
cana-5102	261	5	datasets	dataset	NOUN
cana-5102	261	6	,	,	PUNCT
cana-5102	261	7	such	such	ADJ
cana-5102	261	8	as	as	ADP
cana-5102	261	9	the	the	DET
cana-5102	261	10	64x	64x	NOUN
cana-5102	261	11	,	,	PUNCT
cana-5102	261	12	generally	generally	ADV
cana-5102	261	13	yield	yield	VERB
cana-5102	261	14	better	well	ADJ
cana-5102	261	15	results	result	NOUN
cana-5102	261	16	,	,	PUNCT
cana-5102	261	17	well	well	ADV
cana-5102	261	18	-	-	PUNCT
cana-5102	261	19	optimized	optimize	VERB
cana-5102	261	20	models	model	NOUN
cana-5102	261	21	,	,	PUNCT
cana-5102	261	22	exemplified	exemplify	VERB
cana-5102	261	23	by	by	ADP
cana-5102	261	24	model	model	NOUN
cana-5102	261	25	3	3	NUM
cana-5102	261	26	,	,	PUNCT
cana-5102	261	27	can	can	AUX
cana-5102	261	28	still	still	ADV
cana-5102	261	29	achieve	achieve	VERB
cana-5102	261	30	commendable	commendable	ADJ
cana-5102	261	31	accuracy	accuracy	NOUN
cana-5102	261	32	on	on	ADP
cana-5102	261	33	lower	low	ADJ
cana-5102	261	34	-	-	PUNCT
cana-5102	261	35	resolution	resolution	NOUN
cana-5102	261	36	datasets	dataset	NOUN
cana-5102	261	37	like	like	ADP
cana-5102	261	38	the	the	DET
cana-5102	261	39	28x	28x	NOUN
cana-5102	261	40	.	.	PUNCT
cana-5102	262	1	fig	fig	PROPN
cana-5102	262	2	4	4	NUM
cana-5102	262	3	:	:	PUNCT
cana-5102	262	4	model	model	NOUN
cana-5102	262	5	accuracy	accuracy	NOUN
cana-5102	262	6	while	while	SCONJ
cana-5102	262	7	training	training	NOUN
cana-5102	262	8	of	of	ADP
cana-5102	262	9	all	all	DET
cana-5102	262	10	models	model	NOUN
cana-5102	262	11	with	with	ADP
cana-5102	262	12	28x	28x	NOUN
cana-5102	262	13	dataset	dataset	NOUN
cana-5102	262	14	and	and	CCONJ
cana-5102	262	15	64x	64x	NUM
cana-5102	262	16	dataset	dataset	VERB
cana-5102	262	17	communications	communication	NOUN
cana-5102	262	18	on	on	ADP
cana-5102	262	19	applied	apply	VERB
cana-5102	262	20	nonlinear	nonlinear	ADJ
cana-5102	262	21	analysis	analysis	NOUN
cana-5102	262	22	issn	issn	NOUN
cana-5102	262	23	:	:	PUNCT
cana-5102	262	24	1074	1074	NUM
cana-5102	262	25	-	-	PUNCT
cana-5102	262	26	133x	133x	NUM
cana-5102	262	27	vol	vol	NOUN
cana-5102	262	28	32	32	NUM
cana-5102	262	29	no	no	NOUN
cana-5102	262	30	.	.	PUNCT
cana-5102	263	1	icmasd	icmasd	NOUN
cana-5102	263	2	(	(	PUNCT
cana-5102	263	3	2025	2025	NUM
cana-5102	263	4	)	)	PUNCT
cana-5102	263	5	767	767	NUM
cana-5102	263	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-5102	263	7	fig	fig	NOUN
cana-5102	263	8	5	5	NUM
cana-5102	263	9	:	:	PUNCT
cana-5102	263	10	model	model	NOUN
cana-5102	263	11	loss	loss	NOUN
cana-5102	263	12	while	while	SCONJ
cana-5102	263	13	training	training	NOUN
cana-5102	263	14	of	of	ADP
cana-5102	263	15	all	all	DET
cana-5102	263	16	models	model	NOUN
cana-5102	263	17	with	with	ADP
cana-5102	263	18	28x	28x	NOUN
cana-5102	263	19	dataset	dataset	NOUN
cana-5102	263	20	and	and	CCONJ
cana-5102	263	21	64x	64x	NUM
cana-5102	263	22	dataset	dataset	ADJ
cana-5102	263	23	conclusion	conclusion	NOUN
cana-5102	263	24	the	the	DET
cana-5102	263	25	training	training	NOUN
cana-5102	263	26	outcomes	outcome	NOUN
cana-5102	263	27	of	of	ADP
cana-5102	263	28	models	model	NOUN
cana-5102	263	29	utilizing	utilize	VERB
cana-5102	263	30	both	both	CCONJ
cana-5102	263	31	the	the	DET
cana-5102	263	32	64x	64x	NOUN
cana-5102	263	33	and	and	CCONJ
cana-5102	263	34	28x	28x	NOUN
cana-5102	263	35	datasets	dataset	NOUN
cana-5102	263	36	underscore	underscore	VERB
cana-5102	263	37	the	the	DET
cana-5102	263	38	significant	significant	ADJ
cana-5102	263	39	influence	influence	NOUN
cana-5102	263	40	of	of	ADP
cana-5102	263	41	image	image	NOUN
cana-5102	263	42	resolution	resolution	NOUN
cana-5102	263	43	on	on	ADP
cana-5102	263	44	the	the	DET
cana-5102	263	45	performance	performance	NOUN
cana-5102	263	46	of	of	ADP
cana-5102	263	47	optical	optical	ADJ
cana-5102	263	48	character	character	NOUN
cana-5102	263	49	recognition	recognition	NOUN
cana-5102	263	50	(	(	PUNCT
cana-5102	263	51	ocr	ocr	ADJ
cana-5102	263	52	)	)	PUNCT
cana-5102	263	53	systems	system	NOUN
cana-5102	263	54	.	.	PUNCT
cana-5102	264	1	generally	generally	ADV
cana-5102	264	2	,	,	PUNCT
cana-5102	264	3	models	model	NOUN
cana-5102	264	4	trained	train	VERB
cana-5102	264	5	on	on	ADP
cana-5102	264	6	the	the	DET
cana-5102	264	7	64x	64x	NOUN
cana-5102	264	8	dataset	dataset	NOUN
cana-5102	264	9	demonstrate	demonstrate	VERB
cana-5102	264	10	superior	superior	ADJ
cana-5102	264	11	accuracy	accuracy	NOUN
cana-5102	264	12	and	and	CCONJ
cana-5102	264	13	reduced	reduced	ADJ
cana-5102	264	14	loss	loss	NOUN
cana-5102	264	15	,	,	PUNCT
cana-5102	264	16	indicating	indicate	VERB
cana-5102	264	17	that	that	SCONJ
cana-5102	264	18	higher	high	ADJ
cana-5102	264	19	-	-	PUNCT
cana-5102	264	20	resolution	resolution	NOUN
cana-5102	264	21	images	image	NOUN
cana-5102	264	22	facilitate	facilitate	VERB
cana-5102	264	23	more	more	ADV
cana-5102	264	24	effective	effective	ADJ
cana-5102	264	25	feature	feature	NOUN
cana-5102	264	26	extraction	extraction	NOUN
cana-5102	264	27	.	.	PUNCT
cana-5102	265	1	notably	notably	ADV
cana-5102	265	2	,	,	PUNCT
cana-5102	265	3	model	model	NOUN
cana-5102	265	4	3	3	NUM
cana-5102	265	5	consistently	consistently	ADV
cana-5102	265	6	surpasses	surpass	VERB
cana-5102	265	7	its	its	PRON
cana-5102	265	8	counterparts	counterpart	NOUN
cana-5102	265	9	,	,	PUNCT
cana-5102	265	10	achieving	achieve	VERB
cana-5102	265	11	a	a	DET
cana-5102	265	12	final	final	ADJ
cana-5102	265	13	accuracy	accuracy	NOUN
cana-5102	265	14	of	of	ADP
cana-5102	265	15	0.9907	0.9907	NUM
cana-5102	265	16	across	across	ADP
cana-5102	265	17	both	both	DET
cana-5102	265	18	datasets	dataset	NOUN
cana-5102	265	19	,	,	PUNCT
cana-5102	265	20	which	which	PRON
cana-5102	265	21	suggests	suggest	VERB
cana-5102	265	22	that	that	SCONJ
cana-5102	265	23	a	a	DET
cana-5102	265	24	well	well	ADV
cana-5102	265	25	-	-	PUNCT
cana-5102	265	26	optimized	optimize	VERB
cana-5102	265	27	architecture	architecture	NOUN
cana-5102	265	28	can	can	AUX
cana-5102	265	29	maintain	maintain	VERB
cana-5102	265	30	high	high	ADJ
cana-5102	265	31	performance	performance	NOUN
cana-5102	265	32	even	even	ADV
cana-5102	265	33	with	with	ADP
cana-5102	265	34	lower	low	ADJ
cana-5102	265	35	-	-	PUNCT
cana-5102	265	36	resolution	resolution	NOUN
cana-5102	265	37	inputs	input	NOUN
cana-5102	265	38	.	.	PUNCT
cana-5102	266	1	conversely	conversely	ADV
cana-5102	266	2	,	,	PUNCT
cana-5102	266	3	models	model	NOUN
cana-5102	266	4	such	such	ADJ
cana-5102	266	5	as	as	ADP
cana-5102	266	6	model	model	NOUN
cana-5102	266	7	8	8	NUM
cana-5102	266	8	,	,	PUNCT
cana-5102	266	9	model	model	NOUN
cana-5102	266	10	9	9	NUM
cana-5102	266	11	,	,	PUNCT
cana-5102	266	12	and	and	CCONJ
cana-5102	266	13	model	model	NOUN
cana-5102	266	14	10	10	NUM
cana-5102	266	15	show	show	NOUN
cana-5102	266	16	subpar	subpar	ADJ
cana-5102	266	17	performance	performance	NOUN
cana-5102	266	18	,	,	PUNCT
cana-5102	266	19	implying	imply	VERB
cana-5102	266	20	that	that	SCONJ
cana-5102	266	21	certain	certain	ADJ
cana-5102	266	22	architectural	architectural	ADJ
cana-5102	266	23	designs	design	NOUN
cana-5102	266	24	or	or	CCONJ
cana-5102	266	25	hyperparameter	hyperparameter	NOUN
cana-5102	266	26	settings	setting	NOUN
cana-5102	266	27	may	may	AUX
cana-5102	266	28	not	not	PART
cana-5102	266	29	be	be	AUX
cana-5102	266	30	well	well	ADV
cana-5102	266	31	-	-	PUNCT
cana-5102	266	32	suited	suit	VERB
cana-5102	266	33	for	for	ADP
cana-5102	266	34	this	this	DET
cana-5102	266	35	application	application	NOUN
cana-5102	266	36	.	.	PUNCT
cana-5102	267	1	the	the	DET
cana-5102	267	2	analysis	analysis	NOUN
cana-5102	267	3	also	also	ADV
cana-5102	267	4	indicates	indicate	VERB
cana-5102	267	5	that	that	SCONJ
cana-5102	267	6	the	the	DET
cana-5102	267	7	use	use	NOUN
cana-5102	267	8	of	of	ADP
cana-5102	267	9	relu	relu	NOUN
cana-5102	267	10	activation	activation	NOUN
cana-5102	267	11	functions	function	NOUN
cana-5102	267	12	and	and	CCONJ
cana-5102	267	13	optimizers	optimizer	NOUN
cana-5102	267	14	like	like	ADP
cana-5102	267	15	adam	adam	NOUN
cana-5102	267	16	or	or	CCONJ
cana-5102	267	17	rmsprop	rmsprop	NOUN
cana-5102	267	18	significantly	significantly	ADV
cana-5102	267	19	enhances	enhance	VERB
cana-5102	267	20	performance	performance	NOUN
cana-5102	267	21	when	when	SCONJ
cana-5102	267	22	compared	compare	VERB
cana-5102	267	23	to	to	ADP
cana-5102	267	24	sigmoid	sigmoid	NOUN
cana-5102	267	25	activation	activation	NOUN
cana-5102	267	26	and	and	CCONJ
cana-5102	267	27	sgd	sgd	NOUN
cana-5102	267	28	optimizers	optimizer	NOUN
cana-5102	267	29	.	.	PUNCT
cana-5102	268	1	while	while	SCONJ
cana-5102	268	2	the	the	DET
cana-5102	268	3	64x	64x	NUM
cana-5102	268	4	dataset	dataset	NOUN
cana-5102	268	5	yields	yield	NOUN
cana-5102	268	6	superior	superior	ADJ
cana-5102	268	7	results	result	NOUN
cana-5102	268	8	,	,	PUNCT
cana-5102	268	9	a	a	DET
cana-5102	268	10	well	well	ADV
cana-5102	268	11	-	-	PUNCT
cana-5102	268	12	structured	structure	VERB
cana-5102	268	13	model	model	NOUN
cana-5102	268	14	can	can	AUX
cana-5102	268	15	still	still	ADV
cana-5102	268	16	achieve	achieve	VERB
cana-5102	268	17	commendable	commendable	ADJ
cana-5102	268	18	accuracy	accuracy	NOUN
cana-5102	268	19	with	with	ADP
cana-5102	268	20	lower	low	ADJ
cana-5102	268	21	-	-	PUNCT
cana-5102	268	22	resolution	resolution	NOUN
cana-5102	268	23	images	image	NOUN
cana-5102	268	24	,	,	PUNCT
cana-5102	268	25	making	make	VERB
cana-5102	268	26	it	it	PRON
cana-5102	268	27	a	a	DET
cana-5102	268	28	practical	practical	ADJ
cana-5102	268	29	option	option	NOUN
cana-5102	268	30	in	in	ADP
cana-5102	268	31	scenarios	scenario	NOUN
cana-5102	268	32	with	with	ADP
cana-5102	268	33	limited	limited	ADJ
cana-5102	268	34	resources	resource	NOUN
cana-5102	268	35	.	.	PUNCT
cana-5102	269	1	future	future	ADJ
cana-5102	269	2	scope	scope	NOUN
cana-5102	269	3	of	of	ADP
cana-5102	269	4	research	research	NOUN
cana-5102	269	5	future	future	ADJ
cana-5102	269	6	investigations	investigation	NOUN
cana-5102	269	7	could	could	AUX
cana-5102	269	8	aim	aim	VERB
cana-5102	269	9	to	to	PART
cana-5102	269	10	improve	improve	VERB
cana-5102	269	11	ocr	ocr	ADJ
cana-5102	269	12	performance	performance	NOUN
cana-5102	269	13	by	by	ADP
cana-5102	269	14	delving	delve	VERB
cana-5102	269	15	into	into	ADP
cana-5102	269	16	deeper	deep	ADJ
cana-5102	269	17	neural	neural	ADJ
cana-5102	269	18	network	network	NOUN
cana-5102	269	19	architectures	architecture	NOUN
cana-5102	269	20	,	,	PUNCT
cana-5102	269	21	incorporating	incorporate	VERB
cana-5102	269	22	attention	attention	NOUN
cana-5102	269	23	mechanisms	mechanism	NOUN
cana-5102	269	24	,	,	PUNCT
cana-5102	269	25	and	and	CCONJ
cana-5102	269	26	utilizing	utilize	VERB
cana-5102	269	27	transfer	transfer	NOUN
cana-5102	269	28	learning	learn	VERB
cana-5102	269	29	techniques	technique	NOUN
cana-5102	269	30	.	.	PUNCT
cana-5102	270	1	assessing	assess	VERB
cana-5102	270	2	the	the	DET
cana-5102	270	3	effects	effect	NOUN
cana-5102	270	4	of	of	ADP
cana-5102	270	5	data	datum	NOUN
cana-5102	270	6	augmentation	augmentation	NOUN
cana-5102	270	7	methods	method	NOUN
cana-5102	270	8	and	and	CCONJ
cana-5102	270	9	synthetic	synthetic	ADJ
cana-5102	270	10	data	data	NOUN
cana-5102	270	11	generation	generation	NOUN
cana-5102	270	12	may	may	AUX
cana-5102	270	13	enhance	enhance	VERB
cana-5102	270	14	generalization	generalization	NOUN
cana-5102	270	15	,	,	PUNCT
cana-5102	270	16	especially	especially	ADV
cana-5102	270	17	for	for	ADP
cana-5102	270	18	datasets	dataset	NOUN
cana-5102	270	19	with	with	ADP
cana-5102	270	20	lower	low	ADJ
cana-5102	270	21	resolutions	resolution	NOUN
cana-5102	270	22	.	.	PUNCT
cana-5102	271	1	furthermore	furthermore	ADV
cana-5102	271	2	,	,	PUNCT
cana-5102	271	3	the	the	DET
cana-5102	271	4	development	development	NOUN
cana-5102	271	5	of	of	ADP
cana-5102	271	6	lightweight	lightweight	ADJ
cana-5102	271	7	models	model	NOUN
cana-5102	271	8	tailored	tailor	VERB
cana-5102	271	9	for	for	ADP
cana-5102	271	10	real	real	ADJ
cana-5102	271	11	-	-	PUNCT
cana-5102	271	12	time	time	NOUN
cana-5102	271	13	applications	application	NOUN
cana-5102	271	14	on	on	ADP
cana-5102	271	15	edge	edge	NOUN
cana-5102	271	16	devices	device	NOUN
cana-5102	271	17	could	could	AUX
cana-5102	271	18	broaden	broaden	VERB
cana-5102	271	19	their	their	PRON
cana-5102	271	20	practical	practical	ADJ
cana-5102	271	21	applicability	applicability	NOUN
cana-5102	271	22	.	.	PUNCT
cana-5102	272	1	future	future	ADJ
cana-5102	272	2	research	research	NOUN
cana-5102	272	3	may	may	AUX
cana-5102	272	4	also	also	ADV
cana-5102	272	5	explore	explore	VERB
cana-5102	272	6	hybrid	hybrid	NOUN
cana-5102	272	7	models	model	NOUN
cana-5102	272	8	that	that	PRON
cana-5102	272	9	integrate	integrate	VERB
cana-5102	272	10	convolutional	convolutional	ADJ
cana-5102	272	11	neural	neural	ADJ
cana-5102	272	12	networks	network	NOUN
cana-5102	272	13	(	(	PUNCT
cana-5102	272	14	cnns	cnns	PROPN
cana-5102	272	15	)	)	PUNCT
cana-5102	272	16	with	with	SCONJ
cana-5102	272	17	transformers	transformer	NOUN
cana-5102	272	18	to	to	PART
cana-5102	272	19	capitalize	capitalize	VERB
cana-5102	272	20	on	on	ADP
cana-5102	272	21	the	the	DET
cana-5102	272	22	advantages	advantage	NOUN
cana-5102	272	23	of	of	ADP
cana-5102	272	24	both	both	DET
cana-5102	272	25	frameworks	framework	NOUN
cana-5102	272	26	.	.	PUNCT
cana-5102	273	1	another	another	DET
cana-5102	273	2	promising	promising	ADJ
cana-5102	273	3	avenue	avenue	NOUN
cana-5102	273	4	is	be	AUX
cana-5102	273	5	the	the	DET
cana-5102	273	6	formulation	formulation	NOUN
cana-5102	273	7	of	of	ADP
cana-5102	273	8	adaptive	adaptive	ADJ
cana-5102	273	9	learning	learning	NOUN
cana-5102	273	10	rate	rate	NOUN
cana-5102	273	11	strategies	strategy	NOUN
cana-5102	273	12	to	to	PART
cana-5102	273	13	optimize	optimize	VERB
cana-5102	273	14	training	training	NOUN
cana-5102	273	15	efficiency	efficiency	NOUN
cana-5102	273	16	further	far	ADV
cana-5102	273	17	.	.	PUNCT
cana-5102	274	1	as	as	SCONJ
cana-5102	274	2	advancements	advancement	NOUN
cana-5102	274	3	in	in	ADP
cana-5102	274	4	deep	deep	ADJ
cana-5102	274	5	learning	learning	NOUN
cana-5102	274	6	continue	continue	VERB
cana-5102	274	7	,	,	PUNCT
cana-5102	274	8	ocr	ocr	NOUN
cana-5102	274	9	models	model	NOUN
cana-5102	274	10	can	can	AUX
cana-5102	274	11	be	be	AUX
cana-5102	274	12	refined	refine	VERB
cana-5102	274	13	to	to	PART
cana-5102	274	14	achieve	achieve	VERB
cana-5102	274	15	greater	great	ADJ
cana-5102	274	16	accuracy	accuracy	NOUN
cana-5102	274	17	,	,	PUNCT
cana-5102	274	18	improved	improved	ADJ
cana-5102	274	19	generalization	generalization	NOUN
cana-5102	274	20	,	,	PUNCT
cana-5102	274	21	and	and	CCONJ
cana-5102	274	22	broader	broad	ADJ
cana-5102	274	23	applicability	applicability	NOUN
cana-5102	274	24	across	across	ADP
cana-5102	274	25	diverse	diverse	ADJ
cana-5102	274	26	fields	field	NOUN
cana-5102	274	27	.	.	PUNCT
cana-5102	275	1	communications	communication	NOUN
cana-5102	275	2	on	on	ADP
cana-5102	275	3	applied	apply	VERB
cana-5102	275	4	nonlinear	nonlinear	ADJ
cana-5102	275	5	analysis	analysis	NOUN
cana-5102	275	6	issn	issn	NOUN
cana-5102	275	7	:	:	PUNCT
cana-5102	275	8	1074	1074	NUM
cana-5102	275	9	-	-	PUNCT
cana-5102	275	10	133x	133x	NUM
cana-5102	275	11	vol	vol	NOUN
cana-5102	275	12	32	32	NUM
cana-5102	275	13	no	no	NOUN
cana-5102	275	14	.	.	PUNCT
cana-5102	276	1	icmasd	icmasd	NOUN
cana-5102	276	2	(	(	PUNCT
cana-5102	276	3	2025	2025	NUM
cana-5102	276	4	)	)	PUNCT
cana-5102	277	1	768	768	NUM
cana-5102	277	2	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-5102	277	3	references	reference	NOUN
cana-5102	277	4	[	[	X
cana-5102	277	5	1	1	NUM
cana-5102	277	6	]	]	PUNCT
cana-5102	277	7	s.	s.	PROPN
cana-5102	277	8	mali	mali	PROPN
cana-5102	277	9	,	,	PUNCT
cana-5102	277	10	v.	v.	ADP
cana-5102	277	11	karad	karad	PROPN
cana-5102	277	12	,	,	PUNCT
cana-5102	277	13	g.	g.	PROPN
cana-5102	277	14	g.	g.	PROPN
cana-5102	277	15	rajput	rajput	PROPN
cana-5102	277	16	,	,	PUNCT
cana-5102	277	17	and	and	CCONJ
cana-5102	277	18	s.	s.	PROPN
cana-5102	277	19	m.	m.	PROPN
cana-5102	277	20	mali	mali	PROPN
cana-5102	277	21	,	,	PUNCT
cana-5102	277	22	“	"	PUNCT
cana-5102	277	23	fourier	fourier	NOUN
cana-5102	277	24	descriptor	descriptor	NOUN
cana-5102	277	25	based	base	VERB
cana-5102	277	26	isolated	isolate	VERB
cana-5102	277	27	marathi	marathi	PROPN
cana-5102	277	28	handwritten	handwritten	ADJ
cana-5102	277	29	numeral	numeral	ADJ
cana-5102	277	30	recognition	recognition	NOUN
cana-5102	277	31	,	,	PUNCT
cana-5102	277	32	”	"	PUNCT
cana-5102	277	33	article	article	NOUN
cana-5102	277	34	in	in	ADP
cana-5102	277	35	international	international	ADJ
cana-5102	277	36	journal	journal	NOUN
cana-5102	277	37	of	of	ADP
cana-5102	277	38	computer	computer	NOUN
cana-5102	277	39	applications	application	NOUN
cana-5102	277	40	,	,	PUNCT
cana-5102	277	41	vol	vol	NOUN
cana-5102	277	42	.	.	PROPN
cana-5102	278	1	3	3	NUM
cana-5102	278	2	,	,	PUNCT
cana-5102	278	3	no	no	INTJ
cana-5102	278	4	.	.	NOUN
cana-5102	278	5	4	4	NUM
cana-5102	278	6	,	,	PUNCT
cana-5102	278	7	pp	pp	ADJ
cana-5102	278	8	.	.	PUNCT
cana-5102	279	1	975–8887	975–8887	NUM
cana-5102	279	2	,	,	PUNCT
cana-5102	279	3	2010	2010	NUM
cana-5102	279	4	,	,	PUNCT
cana-5102	279	5	doi	doi	NOUN
cana-5102	279	6	:	:	PUNCT
cana-5102	279	7	10.5120/724	10.5120/724	NUM
cana-5102	279	8	-	-	SYM
cana-5102	279	9	1017	1017	NUM
cana-5102	279	10	.	.	PUNCT
cana-5102	280	1	[	[	X
cana-5102	280	2	2	2	NUM
cana-5102	280	3	]	]	PUNCT
cana-5102	280	4	m.	m.	NOUN
cana-5102	280	5	raus	raus	NOUN
cana-5102	280	6	and	and	CCONJ
cana-5102	280	7	l.	l.	PROPN
cana-5102	280	8	kreft	kreft	PROPN
cana-5102	280	9	,	,	PUNCT
cana-5102	280	10	“	"	PUNCT
cana-5102	280	11	reading	read	VERB
cana-5102	280	12	car	car	NOUN
cana-5102	280	13	license	license	NOUN
cana-5102	280	14	plates	plate	NOUN
cana-5102	280	15	by	by	ADP
cana-5102	280	16	the	the	DET
cana-5102	280	17	use	use	NOUN
cana-5102	280	18	of	of	ADP
cana-5102	280	19	artificial	artificial	ADJ
cana-5102	280	20	neural	neural	ADJ
cana-5102	280	21	networks	network	NOUN
cana-5102	280	22	,	,	PUNCT
cana-5102	280	23	”	"	PUNCT
cana-5102	280	24	midwest	midwest	NOUN
cana-5102	280	25	symposium	symposium	NOUN
cana-5102	280	26	on	on	ADP
cana-5102	280	27	circuits	circuit	NOUN
cana-5102	280	28	and	and	CCONJ
cana-5102	280	29	systems	system	NOUN
cana-5102	280	30	,	,	PUNCT
cana-5102	280	31	vol	vol	NOUN
cana-5102	280	32	.	.	PROPN
cana-5102	280	33	1	1	NUM
cana-5102	280	34	,	,	PUNCT
cana-5102	280	35	pp	pp	ADJ
cana-5102	280	36	.	.	PUNCT
cana-5102	281	1	538–541	538–541	NUM
cana-5102	281	2	,	,	PUNCT
cana-5102	281	3	1995	1995	NUM
cana-5102	281	4	,	,	PUNCT
cana-5102	281	5	doi	doi	NOUN
cana-5102	281	6	:	:	PUNCT
cana-5102	281	7	10.1109	10.1109	NUM
cana-5102	281	8	/	/	SYM
cana-5102	281	9	mwscas.1995.504495	mwscas.1995.504495	NUM
cana-5102	281	10	.	.	PUNCT
cana-5102	282	1	[	[	X
cana-5102	282	2	3	3	X
cana-5102	282	3	]	]	X
cana-5102	282	4	u.	u.	NOUN
cana-5102	282	5	pal	pal	PROPN
cana-5102	282	6	,	,	PUNCT
cana-5102	282	7	t.	t.	PROPN
cana-5102	282	8	wakabayashi	wakabayashi	PROPN
cana-5102	282	9	,	,	PUNCT
cana-5102	282	10	n.	n.	PROPN
cana-5102	282	11	sharma	sharma	PROPN
cana-5102	282	12	,	,	PUNCT
cana-5102	282	13	and	and	CCONJ
cana-5102	282	14	f.	f.	PROPN
cana-5102	282	15	kimura	kimura	PROPN
cana-5102	282	16	,	,	PUNCT
cana-5102	282	17	“	"	PUNCT
cana-5102	282	18	handwritten	handwritten	ADJ
cana-5102	282	19	numeral	numeral	ADJ
cana-5102	282	20	recognition	recognition	NOUN
cana-5102	282	21	of	of	ADP
cana-5102	282	22	six	six	NUM
cana-5102	282	23	popular	popular	ADJ
cana-5102	282	24	indian	indian	ADJ
cana-5102	282	25	scripts	script	NOUN
cana-5102	282	26	,	,	PUNCT
cana-5102	282	27	”	"	PUNCT
cana-5102	282	28	proceedings	proceeding	NOUN
cana-5102	282	29	of	of	ADP
cana-5102	282	30	the	the	DET
cana-5102	282	31	international	international	ADJ
cana-5102	282	32	conference	conference	NOUN
cana-5102	282	33	on	on	ADP
cana-5102	282	34	document	document	NOUN
cana-5102	282	35	analysis	analysis	NOUN
cana-5102	282	36	and	and	CCONJ
cana-5102	282	37	recognition	recognition	NOUN
cana-5102	282	38	,	,	PUNCT
cana-5102	282	39	icdar	icdar	NOUN
cana-5102	282	40	,	,	PUNCT
cana-5102	282	41	vol	vol	NOUN
cana-5102	282	42	.	.	PROPN
cana-5102	282	43	2	2	NUM
cana-5102	282	44	,	,	PUNCT
cana-5102	282	45	pp	pp	ADJ
cana-5102	282	46	.	.	PUNCT
cana-5102	283	1	749–753	749–753	NUM
cana-5102	283	2	,	,	PUNCT
cana-5102	283	3	2007	2007	NUM
cana-5102	283	4	,	,	PUNCT
cana-5102	283	5	doi	doi	NOUN
cana-5102	283	6	:	:	PUNCT
cana-5102	283	7	10.1109	10.1109	NUM
cana-5102	283	8	/	/	SYM
cana-5102	283	9	icdar.2007.4377015	icdar.2007.4377015	NOUN
cana-5102	283	10	.	.	PUNCT
cana-5102	284	1	[	[	X
cana-5102	284	2	4	4	X
cana-5102	284	3	]	]	X
cana-5102	284	4	p.	p.	NOUN
cana-5102	284	5	darshni	darshni	PROPN
cana-5102	284	6	,	,	PUNCT
cana-5102	284	7	b.	b.	PROPN
cana-5102	284	8	s.	s.	PROPN
cana-5102	284	9	dhaliwal	dhaliwal	PROPN
cana-5102	284	10	,	,	PUNCT
cana-5102	284	11	r.	r.	PROPN
cana-5102	284	12	kumar	kumar	PROPN
cana-5102	284	13	,	,	PUNCT
cana-5102	284	14	v.	v.	ADP
cana-5102	284	15	a.	a.	NOUN
cana-5102	284	16	balogun	balogun	PROPN
cana-5102	284	17	,	,	PUNCT
cana-5102	284	18	s.	s.	PROPN
cana-5102	284	19	singh	singh	PROPN
cana-5102	284	20	,	,	PUNCT
cana-5102	284	21	and	and	CCONJ
cana-5102	284	22	c.	c.	PROPN
cana-5102	284	23	i.	i.	PROPN
cana-5102	284	24	pruncu	pruncu	PROPN
cana-5102	284	25	,	,	PUNCT
cana-5102	284	26	“	"	PUNCT
cana-5102	284	27	artificial	artificial	ADJ
cana-5102	284	28	neural	neural	ADJ
cana-5102	284	29	network	network	NOUN
cana-5102	284	30	based	base	VERB
cana-5102	284	31	character	character	NOUN
cana-5102	284	32	recognition	recognition	NOUN
cana-5102	284	33	using	use	VERB
cana-5102	284	34	scilab	scilab	PROPN
cana-5102	284	35	,	,	PUNCT
cana-5102	284	36	”	"	PUNCT
cana-5102	284	37	multimed	multime	VERB
cana-5102	284	38	tools	tool	NOUN
cana-5102	284	39	appl	appl	NOUN
cana-5102	284	40	,	,	PUNCT
cana-5102	284	41	vol	vol	NOUN
cana-5102	284	42	.	.	PROPN
cana-5102	284	43	82	82	NUM
cana-5102	284	44	,	,	PUNCT
cana-5102	284	45	no	no	INTJ
cana-5102	284	46	.	.	NOUN
cana-5102	284	47	2	2	NUM
cana-5102	284	48	,	,	PUNCT
cana-5102	284	49	pp	pp	ADV
cana-5102	284	50	.	.	PUNCT
cana-5102	284	51	2517–2538	2517–2538	NUM
cana-5102	284	52	,	,	PUNCT
cana-5102	284	53	jan	jan	PROPN
cana-5102	284	54	.	.	PROPN
cana-5102	284	55	2023	2023	NUM
cana-5102	284	56	,	,	PUNCT
cana-5102	284	57	doi	doi	NOUN
cana-5102	284	58	:	:	PUNCT
cana-5102	284	59	10.1007	10.1007	NUM
cana-5102	284	60	/	/	SYM
cana-5102	284	61	s11042	s11042	PROPN
cana-5102	284	62	-	-	PUNCT
cana-5102	284	63	022	022	NUM
cana-5102	284	64	-	-	PUNCT
cana-5102	284	65	13082	13082	NUM
cana-5102	284	66	-	-	PUNCT
cana-5102	284	67	w	w	NOUN
cana-5102	284	68	/	/	SYM
cana-5102	284	69	figures/11	figures/11	NOUN
cana-5102	284	70	.	.	PUNCT
cana-5102	285	1	[	[	X
cana-5102	285	2	5	5	NUM
cana-5102	285	3	]	]	PUNCT
cana-5102	285	4	m.	m.	NOUN
cana-5102	285	5	s.	s.	PROPN
cana-5102	285	6	sonawane	sonawane	PROPN
cana-5102	285	7	and	and	CCONJ
cana-5102	285	8	c.	c.	PROPN
cana-5102	285	9	a.	a.	PROPN
cana-5102	285	10	dhawale	dhawale	PROPN
cana-5102	285	11	,	,	PUNCT
cana-5102	285	12	“	"	PUNCT
cana-5102	285	13	evaluation	evaluation	NOUN
cana-5102	285	14	of	of	ADP
cana-5102	285	15	character	character	NOUN
cana-5102	285	16	recognisers	recogniser	NOUN
cana-5102	285	17	:	:	PUNCT
cana-5102	285	18	artificial	artificial	ADJ
cana-5102	285	19	neural	neural	ADJ
cana-5102	285	20	network	network	NOUN
cana-5102	285	21	and	and	CCONJ
cana-5102	285	22	nearest	near	ADJ
cana-5102	285	23	neighbour	neighbour	ADJ
cana-5102	285	24	approach	approach	NOUN
cana-5102	285	25	,	,	PUNCT
cana-5102	285	26	”	"	PUNCT
cana-5102	285	27	proceedings	proceeding	NOUN
cana-5102	285	28	2015	2015	NUM
cana-5102	285	29	ieee	ieee	NOUN
cana-5102	285	30	international	international	ADJ
cana-5102	285	31	conference	conference	NOUN
cana-5102	285	32	on	on	ADP
cana-5102	285	33	computational	computational	ADJ
cana-5102	285	34	intelligence	intelligence	NOUN
cana-5102	285	35	and	and	CCONJ
cana-5102	285	36	communication	communication	NOUN
cana-5102	285	37	technology	technology	NOUN
cana-5102	285	38	,	,	PUNCT
cana-5102	285	39	cict	cict	NOUN
cana-5102	285	40	2015	2015	NUM
cana-5102	285	41	,	,	PUNCT
cana-5102	285	42	pp	pp	ADJ
cana-5102	285	43	.	.	PUNCT
cana-5102	286	1	129–132	129–132	NUM
cana-5102	286	2	,	,	PUNCT
cana-5102	286	3	apr	apr	NOUN
cana-5102	286	4	.	.	PROPN
cana-5102	286	5	2015	2015	NUM
cana-5102	286	6	,	,	PUNCT
cana-5102	286	7	doi	doi	NOUN
cana-5102	286	8	:	:	PUNCT
cana-5102	286	9	10.1109	10.1109	NUM
cana-5102	286	10	/	/	SYM
cana-5102	286	11	cict.2015.30	cict.2015.30	NOUN
cana-5102	286	12	.	.	PUNCT
cana-5102	287	1	[	[	X
cana-5102	287	2	6	6	NUM
cana-5102	287	3	]	]	PUNCT
cana-5102	287	4	a.	a.	NOUN
cana-5102	287	5	g.	g.	PROPN
cana-5102	287	6	ramakrishnan	ramakrishnan	PROPN
cana-5102	287	7	,	,	PUNCT
cana-5102	287	8	“	"	PUNCT
cana-5102	287	9	simultaneous	simultaneous	ADJ
cana-5102	287	10	recognition	recognition	NOUN
cana-5102	287	11	of	of	ADP
cana-5102	287	12	tamil	tamil	PROPN
cana-5102	287	13	and	and	CCONJ
cana-5102	287	14	roman	roman	ADJ
cana-5102	287	15	scripts	script	NOUN
cana-5102	287	16	”	"	PUNCT
cana-5102	287	17	.	.	PUNCT
cana-5102	288	1	[	[	X
cana-5102	288	2	7	7	X
cana-5102	288	3	]	]	X
cana-5102	288	4	d.	d.	PROPN
cana-5102	288	5	nasien	nasien	PROPN
cana-5102	288	6	,	,	PUNCT
cana-5102	288	7	h.	h.	PROPN
cana-5102	288	8	haron	haron	PROPN
cana-5102	288	9	,	,	PUNCT
cana-5102	288	10	and	and	CCONJ
cana-5102	288	11	s.	s.	PROPN
cana-5102	288	12	s.	s.	PROPN
cana-5102	288	13	yuhaniz	yuhaniz	PROPN
cana-5102	288	14	,	,	PUNCT
cana-5102	288	15	“	"	PUNCT
cana-5102	288	16	support	support	NOUN
cana-5102	288	17	vector	vector	NOUN
cana-5102	288	18	machine	machine	NOUN
cana-5102	288	19	(	(	PUNCT
cana-5102	288	20	svm	svm	PROPN
cana-5102	288	21	)	)	PUNCT
cana-5102	288	22	for	for	ADP
cana-5102	288	23	english	english	ADJ
cana-5102	288	24	handwritten	handwritten	ADJ
cana-5102	288	25	character	character	NOUN
cana-5102	288	26	recognition	recognition	NOUN
cana-5102	288	27	,	,	PUNCT
cana-5102	288	28	”	"	PUNCT
cana-5102	288	29	2010	2010	NUM
cana-5102	288	30	2nd	2nd	ADJ
cana-5102	288	31	international	international	ADJ
cana-5102	288	32	conference	conference	NOUN
cana-5102	288	33	on	on	ADP
cana-5102	288	34	computer	computer	NOUN
cana-5102	288	35	engineering	engineering	NOUN
cana-5102	288	36	and	and	CCONJ
cana-5102	288	37	applications	application	NOUN
cana-5102	288	38	,	,	PUNCT
cana-5102	288	39	iccea	iccea	NOUN
cana-5102	288	40	2010	2010	NUM
cana-5102	288	41	,	,	PUNCT
cana-5102	288	42	vol	vol	NOUN
cana-5102	288	43	.	.	PROPN
cana-5102	288	44	1	1	NUM
cana-5102	288	45	,	,	PUNCT
cana-5102	288	46	pp	pp	ADJ
cana-5102	288	47	.	.	PUNCT
cana-5102	289	1	249–252	249–252	NUM
cana-5102	289	2	,	,	PUNCT
cana-5102	289	3	2010	2010	NUM
cana-5102	289	4	,	,	PUNCT
cana-5102	289	5	doi	doi	NOUN
cana-5102	289	6	:	:	PUNCT
cana-5102	289	7	10.1109	10.1109	NUM
cana-5102	289	8	/	/	SYM
cana-5102	289	9	iccea.2010.56	iccea.2010.56	VERB
cana-5102	289	10	.	.	PUNCT
cana-5102	290	1	[	[	X
cana-5102	290	2	8	8	NUM
cana-5102	290	3	]	]	X
cana-5102	290	4	y.	y.	PROPN
cana-5102	290	5	babiker	babiker	PROPN
cana-5102	290	6	hamdan	hamdan	PROPN
cana-5102	290	7	,	,	PUNCT
cana-5102	290	8	“	"	PUNCT
cana-5102	290	9	construction	construction	NOUN
cana-5102	290	10	of	of	ADP
cana-5102	290	11	statistical	statistical	ADJ
cana-5102	290	12	svm	svm	NOUN
cana-5102	290	13	based	base	VERB
cana-5102	290	14	recognition	recognition	NOUN
cana-5102	290	15	model	model	NOUN
cana-5102	290	16	for	for	ADP
cana-5102	290	17	handwritten	handwritten	ADJ
cana-5102	290	18	character	character	NOUN
cana-5102	290	19	recognition	recognition	NOUN
cana-5102	290	20	,	,	PUNCT
cana-5102	290	21	”	"	PUNCT
cana-5102	290	22	journal	journal	NOUN
cana-5102	290	23	of	of	ADP
cana-5102	290	24	information	information	NOUN
cana-5102	290	25	technology	technology	NOUN
cana-5102	290	26	and	and	CCONJ
cana-5102	290	27	digital	digital	ADJ
cana-5102	290	28	world	world	NOUN
cana-5102	290	29	,	,	PUNCT
cana-5102	290	30	vol	vol	NOUN
cana-5102	290	31	.	.	PROPN
cana-5102	290	32	03	03	NUM
cana-5102	290	33	,	,	PUNCT
cana-5102	290	34	no	no	INTJ
cana-5102	290	35	.	.	NOUN
cana-5102	290	36	02	02	NUM
cana-5102	290	37	,	,	PUNCT
cana-5102	290	38	2021	2021	NUM
cana-5102	290	39	,	,	PUNCT
cana-5102	290	40	doi	doi	NOUN
cana-5102	290	41	:	:	PUNCT
cana-5102	290	42	10.36548	10.36548	NUM
cana-5102	290	43	/	/	SYM
cana-5102	290	44	jitdw.2021.2.003	jitdw.2021.2.003	PROPN
cana-5102	290	45	.	.	PUNCT
cana-5102	291	1	[	[	X
cana-5102	291	2	9	9	NUM
cana-5102	291	3	]	]	X
cana-5102	291	4	n.	n.	NOUN
cana-5102	291	5	m.	m.	PROPN
cana-5102	291	6	noor	noor	PROPN
cana-5102	291	7	,	,	PUNCT
cana-5102	291	8	m.	m.	NOUN
cana-5102	291	9	razaz	razaz	PROPN
cana-5102	291	10	,	,	PUNCT
cana-5102	291	11	and	and	CCONJ
cana-5102	291	12	p.	p.	PROPN
cana-5102	291	13	manley	manley	PROPN
cana-5102	291	14	-	-	PUNCT
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cana-5102	291	26	character	character	NOUN
cana-5102	291	27	recognition	recognition	NOUN
cana-5102	291	28	,	,	PUNCT
cana-5102	291	29	”	"	PUNCT
cana-5102	291	30	proceedings	proceeding	NOUN
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cana-5102	291	32	conference	conference	NOUN
cana-5102	291	33	on	on	ADP
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cana-5102	292	2	,	,	PUNCT
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cana-5102	294	15	mobile	mobile	ADJ
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cana-5102	295	3	]	]	PUNCT
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cana-5102	304	9	,	,	PUNCT
cana-5102	304	10	“	"	PUNCT
cana-5102	304	11	a	a	DET
cana-5102	304	12	wearable	wearable	ADJ
cana-5102	304	13	real	real	ADJ
cana-5102	304	14	-	-	PUNCT
cana-5102	304	15	time	time	NOUN
cana-5102	304	16	character	character	NOUN
cana-5102	304	17	recognition	recognition	NOUN
cana-5102	304	18	system	system	NOUN
cana-5102	304	19	based	base	VERB
cana-5102	304	20	on	on	ADP
cana-5102	304	21	edge	edge	NOUN
cana-5102	304	22	computing	computing	NOUN
cana-5102	304	23	-	-	PUNCT
cana-5102	304	24	enabled	enable	VERB
cana-5102	304	25	deep	deep	ADJ
cana-5102	304	26	learning	learning	NOUN
cana-5102	304	27	for	for	ADP
cana-5102	304	28	air	air	NOUN
cana-5102	304	29	-	-	PUNCT
cana-5102	304	30	writing	writing	NOUN
cana-5102	304	31	,	,	PUNCT
cana-5102	304	32	”	"	PUNCT
cana-5102	304	33	j	j	PROPN
cana-5102	304	34	sens	sens	PROPN
cana-5102	304	35	,	,	PUNCT
cana-5102	304	36	vol	vol	NOUN
cana-5102	304	37	.	.	NOUN
cana-5102	304	38	2022	2022	NUM
cana-5102	304	39	,	,	PUNCT
cana-5102	304	40	2022	2022	NUM
cana-5102	304	41	,	,	PUNCT
cana-5102	304	42	doi	doi	NOUN
cana-5102	304	43	:	:	PUNCT
cana-5102	304	44	10.1155/2022/8507706	10.1155/2022/8507706	NUM
cana-5102	304	45	.	.	PUNCT
cana-5102	305	1	[	[	X
cana-5102	305	2	17	17	NUM
cana-5102	305	3	]	]	X
cana-5102	305	4	n.	n.	NOUN
cana-5102	305	5	a.	a.	NOUN
cana-5102	305	6	jebril	jebril	NOUN
cana-5102	305	7	,	,	PUNCT
cana-5102	305	8	h.	h.	PROPN
cana-5102	305	9	r.	r.	PROPN
cana-5102	305	10	al	al	PROPN
cana-5102	305	11	-	-	PROPN
cana-5102	305	12	zoubi	zoubi	PROPN
cana-5102	305	13	,	,	PUNCT
cana-5102	305	14	and	and	CCONJ
cana-5102	305	15	q.	q.	PROPN
cana-5102	305	16	abu	abu	PROPN
cana-5102	305	17	al	al	PROPN
cana-5102	305	18	-	-	PUNCT
cana-5102	305	19	haija	haija	PROPN
cana-5102	305	20	,	,	PUNCT
cana-5102	305	21	“	"	PUNCT
cana-5102	305	22	recognition	recognition	NOUN
cana-5102	305	23	of	of	ADP
cana-5102	305	24	handwritten	handwritten	ADJ
cana-5102	305	25	arabic	arabic	ADJ
cana-5102	305	26	characters	character	NOUN
cana-5102	305	27	using	use	VERB
cana-5102	305	28	histograms	histogram	NOUN
cana-5102	305	29	of	of	ADP
cana-5102	305	30	oriented	orient	VERB
cana-5102	305	31	gradient	gradient	NOUN
cana-5102	305	32	(	(	PUNCT
cana-5102	305	33	hog	hog	PROPN
cana-5102	305	34	)	)	PUNCT
cana-5102	305	35	,	,	PUNCT
cana-5102	305	36	”	"	PUNCT
cana-5102	305	37	pattern	pattern	NOUN
cana-5102	305	38	recognition	recognition	NOUN
cana-5102	305	39	and	and	CCONJ
cana-5102	305	40	image	image	NOUN
cana-5102	305	41	analysis	analysis	NOUN
cana-5102	305	42	,	,	PUNCT
cana-5102	305	43	vol	vol	NOUN
cana-5102	305	44	.	.	PROPN
cana-5102	305	45	28	28	NUM
cana-5102	305	46	,	,	PUNCT
cana-5102	305	47	no	no	INTJ
cana-5102	305	48	.	.	NOUN
cana-5102	305	49	2	2	NUM
cana-5102	305	50	,	,	PUNCT
cana-5102	305	51	pp	pp	ADJ
cana-5102	305	52	.	.	PUNCT
cana-5102	306	1	321–345	321–345	NUM
cana-5102	306	2	,	,	PUNCT
cana-5102	306	3	apr	apr	NOUN
cana-5102	306	4	.	.	PROPN
cana-5102	306	5	2018	2018	NUM
cana-5102	306	6	,	,	PUNCT
cana-5102	306	7	doi	doi	NOUN
cana-5102	306	8	:	:	PUNCT
cana-5102	306	9	10.1134	10.1134	NUM
cana-5102	306	10	/	/	SYM
cana-5102	306	11	s1054661818020141	s1054661818020141	NOUN
cana-5102	306	12	/	/	SYM
cana-5102	306	13	metrics	metric	NOUN
cana-5102	306	14	.	.	PUNCT
cana-5102	307	1	[	[	X
cana-5102	307	2	18	18	NUM
cana-5102	307	3	]	]	X
cana-5102	307	4	n.	n.	NOUN
cana-5102	307	5	lamghari	lamghari	PROPN
cana-5102	307	6	,	,	PUNCT
cana-5102	307	7	m.	m.	PROPN
cana-5102	307	8	e.	e.	PROPN
cana-5102	307	9	h.	h.	PROPN
cana-5102	307	10	charaf	charaf	PROPN
cana-5102	307	11	,	,	PUNCT
cana-5102	307	12	and	and	CCONJ
cana-5102	307	13	s.	s.	PROPN
cana-5102	307	14	raghay	raghay	PROPN
cana-5102	307	15	,	,	PUNCT
cana-5102	307	16	“	"	PUNCT
cana-5102	307	17	hybrid	hybrid	ADJ
cana-5102	307	18	feature	feature	NOUN
cana-5102	307	19	vector	vector	NOUN
cana-5102	307	20	for	for	ADP
cana-5102	307	21	the	the	DET
cana-5102	307	22	recognition	recognition	NOUN
cana-5102	307	23	of	of	ADP
cana-5102	307	24	arabic	arabic	ADJ
cana-5102	307	25	handwritten	handwritten	ADJ
cana-5102	307	26	characters	character	NOUN
cana-5102	307	27	using	use	VERB
cana-5102	307	28	feed	feed	NOUN
cana-5102	307	29	-	-	PUNCT
cana-5102	307	30	forward	forward	ADV
cana-5102	307	31	neural	neural	ADJ
cana-5102	307	32	network	network	NOUN
cana-5102	307	33	,	,	PUNCT
cana-5102	307	34	”	"	PUNCT
cana-5102	307	35	arab	arab	PROPN
cana-5102	307	36	j	j	PROPN
cana-5102	307	37	sci	sci	PROPN
cana-5102	307	38	eng	eng	PROPN
cana-5102	307	39	,	,	PUNCT
cana-5102	307	40	vol	vol	NOUN
cana-5102	307	41	.	.	PROPN
cana-5102	307	42	43	43	NUM
cana-5102	307	43	,	,	PUNCT
cana-5102	307	44	no	no	INTJ
cana-5102	307	45	.	.	NOUN
cana-5102	307	46	12	12	NUM
cana-5102	307	47	,	,	PUNCT
cana-5102	307	48	pp	pp	ADJ
cana-5102	307	49	.	.	PUNCT
cana-5102	307	50	7031–7039	7031–7039	NOUN
cana-5102	307	51	,	,	PUNCT
cana-5102	307	52	dec	dec	PROPN
cana-5102	307	53	.	.	PROPN
cana-5102	307	54	2018	2018	NUM
cana-5102	307	55	,	,	PUNCT
cana-5102	307	56	doi	doi	NOUN
cana-5102	307	57	:	:	PUNCT
cana-5102	307	58	10.1007	10.1007	NUM
cana-5102	307	59	/	/	SYM
cana-5102	307	60	s13369	s13369	PROPN
cana-5102	307	61	-	-	PUNCT
cana-5102	307	62	017	017	NUM
cana-5102	307	63	-	-	PUNCT
cana-5102	307	64	2969	2969	NUM
cana-5102	307	65	-	-	PUNCT
cana-5102	307	66	1	1	NUM
cana-5102	307	67	/	/	SYM
cana-5102	307	68	metrics	metric	NOUN
cana-5102	307	69	.	.	PUNCT
cana-5102	308	1	[	[	X
cana-5102	308	2	19	19	NUM
cana-5102	308	3	]	]	X
cana-5102	308	4	j.	j.	PROPN
cana-5102	308	5	cowell	cowell	PROPN
cana-5102	308	6	and	and	CCONJ
cana-5102	308	7	f.	f.	PROPN
cana-5102	308	8	hussain	hussain	PROPN
cana-5102	308	9	,	,	PUNCT
cana-5102	308	10	“	"	PUNCT
cana-5102	308	11	amharic	amharic	ADJ
cana-5102	308	12	character	character	NOUN
cana-5102	308	13	recognition	recognition	NOUN
cana-5102	308	14	using	use	VERB
cana-5102	308	15	a	a	DET
cana-5102	308	16	fast	fast	ADJ
cana-5102	308	17	signature	signature	NOUN
cana-5102	308	18	based	base	VERB
cana-5102	308	19	algorithm	algorithm	NOUN
cana-5102	308	20	,	,	PUNCT
cana-5102	308	21	”	"	PUNCT
cana-5102	308	22	proceedings	proceeding	NOUN
cana-5102	308	23	of	of	ADP
cana-5102	308	24	the	the	DET
cana-5102	308	25	international	international	ADJ
cana-5102	308	26	conference	conference	NOUN
cana-5102	308	27	on	on	ADP
cana-5102	308	28	information	information	NOUN
cana-5102	308	29	visualisation	visualisation	NOUN
cana-5102	308	30	,	,	PUNCT
cana-5102	308	31	vol	vol	NOUN
cana-5102	308	32	.	.	PROPN
cana-5102	308	33	2003	2003	NUM
cana-5102	308	34	-	-	SYM
cana-5102	308	35	january	january	PROPN
cana-5102	308	36	,	,	PUNCT
cana-5102	308	37	pp	pp	X
cana-5102	308	38	.	.	PUNCT
cana-5102	309	1	384–389	384–389	NUM
cana-5102	309	2	,	,	PUNCT
cana-5102	309	3	2003	2003	NUM
cana-5102	309	4	,	,	PUNCT
cana-5102	309	5	doi	doi	NOUN
cana-5102	309	6	:	:	PUNCT
cana-5102	309	7	10.1109	10.1109	NUM
cana-5102	309	8	/	/	SYM
cana-5102	309	9	iv.2003.1218014	iv.2003.1218014	NOUN
cana-5102	309	10	.	.	PUNCT
cana-5102	310	1	[	[	X
cana-5102	310	2	20	20	NUM
cana-5102	310	3	]	]	X
cana-5102	310	4	r.	r.	PROPN
cana-5102	310	5	bajaj	bajaj	PROPN
cana-5102	310	6	,	,	PUNCT
cana-5102	310	7	l.	l.	PROPN
cana-5102	310	8	dey	dey	PROPN
cana-5102	310	9	,	,	PUNCT
cana-5102	310	10	and	and	CCONJ
cana-5102	310	11	s.	s.	PROPN
cana-5102	310	12	chaudhury	chaudhury	PROPN
cana-5102	310	13	,	,	PUNCT
cana-5102	310	14	“	"	PUNCT
cana-5102	310	15	devnagari	devnagari	ADJ
cana-5102	310	16	numeral	numeral	ADJ
cana-5102	310	17	recognition	recognition	NOUN
cana-5102	310	18	by	by	ADP
cana-5102	310	19	combining	combine	VERB
cana-5102	310	20	decision	decision	NOUN
cana-5102	310	21	of	of	ADP
cana-5102	310	22	multiple	multiple	ADJ
cana-5102	310	23	connectionist	connectionist	NOUN
cana-5102	310	24	classifiers	classifier	NOUN
cana-5102	310	25	,	,	PUNCT
cana-5102	310	26	”	"	PUNCT
cana-5102	310	27	vol	vol	NOUN
cana-5102	310	28	.	.	PROPN
cana-5102	310	29	27	27	NUM
cana-5102	310	30	,	,	PUNCT
cana-5102	310	31	pp	pp	ADJ
cana-5102	310	32	.	.	PUNCT
cana-5102	311	1	59–72	59–72	NUM
cana-5102	311	2	,	,	PUNCT
cana-5102	311	3	2002	2002	NUM
cana-5102	311	4	.	.	PUNCT
cana-5102	312	1	[	[	X
cana-5102	312	2	21	21	NUM
cana-5102	312	3	]	]	X
cana-5102	312	4	u.	u.	PROPN
cana-5102	312	5	pal	pal	PROPN
cana-5102	312	6	,	,	PUNCT
cana-5102	312	7	s.	s.	PROPN
cana-5102	312	8	chanda	chanda	PROPN
cana-5102	312	9	,	,	PUNCT
cana-5102	312	10	t.	t.	PROPN
cana-5102	312	11	wakabayashi	wakabayashi	PROPN
cana-5102	312	12	,	,	PUNCT
cana-5102	312	13	and	and	CCONJ
cana-5102	312	14	f.	f.	PROPN
cana-5102	312	15	kimura	kimura	PROPN
cana-5102	312	16	,	,	PUNCT
cana-5102	312	17	“	"	PUNCT
cana-5102	312	18	accuracy	accuracy	NOUN
cana-5102	312	19	improvement	improvement	NOUN
cana-5102	312	20	of	of	ADP
cana-5102	312	21	devanagari	devanagari	ADJ
cana-5102	312	22	character	character	NOUN
cana-5102	312	23	recognition	recognition	NOUN
cana-5102	312	24	combining	combine	VERB
cana-5102	312	25	svm	svm	NOUN
cana-5102	312	26	and	and	CCONJ
cana-5102	312	27	mqdf	mqdf	ADJ
cana-5102	312	28	accuracy	accuracy	NOUN
cana-5102	312	29	improvement	improvement	NOUN
cana-5102	312	30	of	of	ADP
cana-5102	312	31	devnagari	devnagari	ADJ
cana-5102	312	32	character	character	NOUN
cana-5102	312	33	recognition	recognition	NOUN
cana-5102	312	34	combining	combine	VERB
cana-5102	312	35	svm	svm	NOUN
cana-5102	312	36	and	and	CCONJ
cana-5102	312	37	mqdf	mqdf	NOUN
cana-5102	312	38	,	,	PUNCT
cana-5102	312	39	”	"	PUNCT
cana-5102	312	40	2008	2008	NUM
cana-5102	312	41	,	,	PUNCT
cana-5102	312	42	accessed	access	VERB
cana-5102	312	43	:	:	PUNCT
cana-5102	312	44	mar	mar	PROPN
cana-5102	312	45	.	.	PROPN
cana-5102	312	46	18	18	NUM
cana-5102	312	47	,	,	PUNCT
cana-5102	312	48	2023	2023	NUM
cana-5102	312	49	.	.	PUNCT
cana-5102	313	1	[	[	X
cana-5102	313	2	22	22	NUM
cana-5102	313	3	]	]	PUNCT
cana-5102	313	4	a.	a.	NOUN
cana-5102	313	5	bwatiramba	bwatiramba	PROPN
cana-5102	313	6	,	,	PUNCT
cana-5102	313	7	s.	s.	PROPN
cana-5102	313	8	venkataraman	venkataraman	PROPN
cana-5102	313	9	,	,	PUNCT
cana-5102	313	10	and	and	CCONJ
cana-5102	313	11	a.	a.	NOUN
cana-5102	313	12	ramjon	ramjon	NOUN
cana-5102	313	13	,	,	PUNCT
cana-5102	313	14	“	"	PUNCT
cana-5102	313	15	handwritten	handwritten	ADJ
cana-5102	313	16	character	character	NOUN
cana-5102	313	17	recognition	recognition	NOUN
cana-5102	313	18	using	use	VERB
cana-5102	313	19	deep	deep	ADJ
cana-5102	313	20	learning	learning	NOUN
cana-5102	313	21	(	(	PUNCT
cana-5102	313	22	convolutional	convolutional	ADJ
cana-5102	313	23	neural	neural	ADJ
cana-5102	313	24	network	network	NOUN
cana-5102	313	25	)	)	PUNCT
cana-5102	313	26	,	,	PUNCT
cana-5102	313	27	”	"	PUNCT
cana-5102	313	28	vol	vol	NOUN
cana-5102	313	29	.	.	PROPN
cana-5102	314	1	14	14	NUM
cana-5102	314	2	,	,	PUNCT
cana-5102	314	3	no	no	INTJ
cana-5102	314	4	.	.	NOUN
cana-5102	314	5	1	1	NUM
cana-5102	314	6	,	,	PUNCT
cana-5102	314	7	p.	p.	NOUN
cana-5102	314	8	2023	2023	NUM
cana-5102	314	9	,	,	PUNCT
cana-5102	314	10	2023	2023	NUM
cana-5102	314	11	,	,	PUNCT
cana-5102	314	12	doi	doi	NOUN
cana-5102	314	13	:	:	PUNCT
cana-5102	314	14	10.7176	10.7176	NUM
cana-5102	314	15	/	/	SYM
cana-5102	314	16	ceis/14	ceis/14	NOUN
cana-5102	314	17	-	-	PUNCT
cana-5102	314	18	1	1	NUM
cana-5102	314	19	-	-	NUM
cana-5102	314	20	05	05	NUM
cana-5102	314	21	.	.	PUNCT
cana-5102	315	1	[	[	X
cana-5102	315	2	23	23	NUM
cana-5102	315	3	]	]	X
cana-5102	315	4	n.	n.	PROPN
cana-5102	315	5	singh	singh	PROPN
cana-5102	315	6	,	,	PUNCT
cana-5102	315	7	“	"	PUNCT
cana-5102	315	8	an	an	DET
cana-5102	315	9	efficient	efficient	ADJ
cana-5102	315	10	approach	approach	NOUN
cana-5102	315	11	for	for	ADP
cana-5102	315	12	handwritten	handwritten	ADJ
cana-5102	315	13	devanagari	devanagari	ADJ
cana-5102	315	14	character	character	NOUN
cana-5102	315	15	recognition	recognition	NOUN
cana-5102	315	16	based	base	VERB
cana-5102	315	17	on	on	ADP
cana-5102	315	18	artificial	artificial	ADJ
cana-5102	315	19	neural	neural	ADJ
cana-5102	315	20	network	network	NOUN
cana-5102	315	21	,	,	PUNCT
cana-5102	315	22	”	"	PUNCT
cana-5102	315	23	2018	2018	NUM
cana-5102	315	24	5th	5th	ADJ
cana-5102	315	25	international	international	ADJ
cana-5102	315	26	conference	conference	NOUN
cana-5102	315	27	on	on	ADP
cana-5102	315	28	signal	signal	NOUN
cana-5102	315	29	processing	processing	NOUN
cana-5102	315	30	and	and	CCONJ
cana-5102	315	31	integrated	integrated	ADJ
cana-5102	315	32	networks	network	NOUN
cana-5102	315	33	,	,	PUNCT
cana-5102	315	34	spin	spin	NOUN
cana-5102	315	35	2018	2018	NUM
cana-5102	315	36	,	,	PUNCT
cana-5102	315	37	pp	pp	ADJ
cana-5102	315	38	.	.	PUNCT
cana-5102	316	1	894–897	894–897	NUM
cana-5102	316	2	,	,	PUNCT
cana-5102	316	3	sep	sep	PROPN
cana-5102	316	4	.	.	PROPN
cana-5102	316	5	2018	2018	NUM
cana-5102	316	6	,	,	PUNCT
cana-5102	316	7	doi	doi	NOUN
cana-5102	316	8	:	:	PUNCT
cana-5102	316	9	10.1109	10.1109	NUM
cana-5102	316	10	/	/	SYM
cana-5102	316	11	spin.2018.8474282	spin.2018.8474282	PROPN
cana-5102	316	12	.	.	PUNCT
cana-5102	317	1	[	[	X
cana-5102	317	2	24	24	NUM
cana-5102	317	3	]	]	PUNCT
cana-5102	317	4	a.	a.	NOUN
cana-5102	317	5	bhattacharyya	bhattacharyya	PROPN
cana-5102	317	6	,	,	PUNCT
cana-5102	317	7	r.	r.	PROPN
cana-5102	317	8	chakraborty	chakraborty	PROPN
cana-5102	317	9	,	,	PUNCT
cana-5102	317	10	s.	s.	PROPN
cana-5102	317	11	saha	saha	PROPN
cana-5102	317	12	,	,	PUNCT
cana-5102	317	13	s.	s.	PROPN
cana-5102	317	14	sen	sen	PROPN
cana-5102	317	15	,	,	PUNCT
cana-5102	317	16	r.	r.	PROPN
cana-5102	317	17	sarkar	sarkar	PROPN
cana-5102	317	18	,	,	PUNCT
cana-5102	317	19	and	and	CCONJ
cana-5102	317	20	k.	k.	PROPN
cana-5102	317	21	roy	roy	PROPN
cana-5102	317	22	,	,	PUNCT
cana-5102	317	23	“	"	PUNCT
cana-5102	317	24	a	a	DET
cana-5102	317	25	two	two	NUM
cana-5102	317	26	-	-	PUNCT
cana-5102	317	27	stage	stage	NOUN
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cana-5102	317	31	method	method	NOUN
cana-5102	317	32	for	for	ADP
cana-5102	317	33	online	online	ADJ
cana-5102	317	34	handwritten	handwritten	ADJ
cana-5102	317	35	bangla	bangla	PROPN
cana-5102	317	36	and	and	CCONJ
cana-5102	317	37	devanagari	devanagari	ADJ
cana-5102	317	38	basic	basic	ADJ
cana-5102	317	39	character	character	NOUN
cana-5102	317	40	recognition	recognition	NOUN
cana-5102	317	41	,	,	PUNCT
cana-5102	317	42	”	"	PUNCT
cana-5102	317	43	sn	sn	PROPN
cana-5102	317	44	comput	comput	PROPN
cana-5102	317	45	sci	sci	PROPN
cana-5102	317	46	,	,	PUNCT
cana-5102	317	47	vol	vol	NOUN
cana-5102	317	48	.	.	PROPN
cana-5102	318	1	3	3	NUM
cana-5102	318	2	,	,	PUNCT
cana-5102	318	3	no	no	INTJ
cana-5102	318	4	.	.	NOUN
cana-5102	318	5	4	4	NUM
cana-5102	318	6	,	,	PUNCT
cana-5102	318	7	pp	pp	PROPN
cana-5102	318	8	.	.	PUNCT
cana-5102	319	1	1–16	1–16	PROPN
cana-5102	319	2	,	,	PUNCT
cana-5102	319	3	jul	jul	PROPN
cana-5102	319	4	.	.	PROPN
cana-5102	319	5	2022	2022	NUM
cana-5102	319	6	,	,	PUNCT
cana-5102	319	7	doi	doi	NOUN
cana-5102	319	8	:	:	PUNCT
cana-5102	319	9	10.1007	10.1007	NUM
cana-5102	319	10	/	/	SYM
cana-5102	319	11	s42979	s42979	PROPN
cana-5102	319	12	-	-	PUNCT
cana-5102	319	13	022	022	NUM
cana-5102	319	14	-	-	PUNCT
cana-5102	319	15	011572	011572	NUM
cana-5102	319	16	/	/	SYM
cana-5102	319	17	metrics	metric	NOUN
cana-5102	319	18	.	.	PUNCT
cana-5102	320	1	[	[	X
cana-5102	320	2	25	25	NUM
cana-5102	320	3	]	]	PUNCT
cana-5102	320	4	s.	s.	PROPN
cana-5102	320	5	d.	d.	PROPN
cana-5102	320	6	connell	connell	PROPN
cana-5102	320	7	,	,	PUNCT
cana-5102	320	8	r.	r.	PROPN
cana-5102	320	9	m.	m.	PROPN
cana-5102	320	10	k.	k.	PROPN
cana-5102	320	11	sinha	sinha	PROPN
cana-5102	320	12	,	,	PUNCT
cana-5102	320	13	and	and	CCONJ
cana-5102	320	14	a.	a.	PROPN
cana-5102	320	15	k.	k.	PROPN
cana-5102	320	16	jain	jain	PROPN
cana-5102	320	17	,	,	PUNCT
cana-5102	320	18	“	"	PUNCT
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cana-5102	320	20	of	of	ADP
cana-5102	320	21	unconstrained	unconstrained	ADJ
cana-5102	320	22	on	on	ADP
cana-5102	320	23	-	-	PUNCT
cana-5102	320	24	line	line	NOUN
cana-5102	320	25	devanagari	devanagari	ADJ
cana-5102	320	26	characters	character	NOUN
cana-5102	320	27	,	,	PUNCT
cana-5102	320	28	”	"	PUNCT
cana-5102	320	29	proceedings	proceeding	NOUN
cana-5102	320	30	international	international	ADJ
cana-5102	320	31	conference	conference	NOUN
cana-5102	320	32	on	on	ADP
cana-5102	320	33	pattern	pattern	NOUN
cana-5102	320	34	recognition	recognition	NOUN
cana-5102	320	35	,	,	PUNCT
cana-5102	320	36	vol	vol	NOUN
cana-5102	320	37	.	.	PROPN
cana-5102	320	38	15	15	NUM
cana-5102	320	39	,	,	PUNCT
cana-5102	320	40	no	no	INTJ
cana-5102	320	41	.	.	NOUN
cana-5102	320	42	2	2	NUM
cana-5102	320	43	,	,	PUNCT
cana-5102	320	44	pp	pp	ADJ
cana-5102	320	45	.	.	PUNCT
cana-5102	321	1	368–371	368–371	NUM
cana-5102	321	2	,	,	PUNCT
cana-5102	321	3	2000	2000	NUM
cana-5102	321	4	,	,	PUNCT
cana-5102	321	5	doi	doi	NOUN
cana-5102	321	6	:	:	PUNCT
cana-5102	321	7	10.1109	10.1109	NUM
cana-5102	321	8	/	/	SYM
cana-5102	321	9	icpr.2000.906089	icpr.2000.906089	NOUN
cana-5102	321	10	.	.	PUNCT
cana-5102	322	1	[	[	X
cana-5102	322	2	26	26	NUM
cana-5102	322	3	]	]	X
cana-5102	322	4	d.	d.	PROPN
cana-5102	322	5	yadav	yadav	PROPN
cana-5102	322	6	,	,	PUNCT
cana-5102	322	7	s.	s.	PROPN
cana-5102	322	8	sánchez	sánchez	PROPN
cana-5102	322	9	-	-	PUNCT
cana-5102	322	10	cuadrado	cuadrado	PROPN
cana-5102	322	11	,	,	PUNCT
cana-5102	322	12	and	and	CCONJ
cana-5102	322	13	j.	j.	PROPN
cana-5102	322	14	morato	morato	PROPN
cana-5102	322	15	,	,	PUNCT
cana-5102	322	16	“	"	PUNCT
cana-5102	322	17	optical	optical	ADJ
cana-5102	322	18	character	character	NOUN
cana-5102	322	19	recognition	recognition	NOUN
cana-5102	322	20	for	for	ADP
cana-5102	322	21	hindi	hindi	NOUN
cana-5102	322	22	language	language	NOUN
cana-5102	322	23	using	use	VERB
cana-5102	322	24	a	a	DET
cana-5102	322	25	neural	neural	ADJ
cana-5102	322	26	-	-	PUNCT
cana-5102	322	27	network	network	NOUN
cana-5102	322	28	approach	approach	NOUN
cana-5102	322	29	,	,	PUNCT
cana-5102	322	30	”	"	PUNCT
cana-5102	322	31	j	j	PROPN
cana-5102	322	32	inf	inf	PROPN
cana-5102	322	33	process	process	NOUN
cana-5102	322	34	syst	syst	NOUN
cana-5102	322	35	,	,	PUNCT
cana-5102	322	36	vol	vol	NOUN
cana-5102	322	37	.	.	PROPN
cana-5102	322	38	9	9	NUM
cana-5102	322	39	,	,	PUNCT
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cana-5102	322	41	.	.	NOUN
cana-5102	322	42	1	1	NUM
cana-5102	322	43	,	,	PUNCT
cana-5102	322	44	2013	2013	NUM
cana-5102	322	45	,	,	PUNCT
cana-5102	322	46	doi	doi	NOUN
cana-5102	322	47	:	:	PUNCT
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cana-5102	322	49	/	/	SYM
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cana-5102	322	51	.	.	PUNCT
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cana-5102	323	3	]	]	PUNCT
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cana-5102	323	6	and	and	CCONJ
cana-5102	323	7	s.	s.	PROPN
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cana-5102	323	9	,	,	PUNCT
cana-5102	323	10	“	"	PUNCT
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cana-5102	323	12	hindi	hindi	NOUN
cana-5102	323	13	character	character	NOUN
cana-5102	323	14	recognition	recognition	NOUN
cana-5102	323	15	using	use	VERB
cana-5102	323	16	k	k	ADJ
cana-5102	323	17	-	-	PUNCT
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cana-5102	323	19	clustering	clustering	NOUN
cana-5102	323	20	and	and	CCONJ
cana-5102	323	21	svm	svm	ADJ
cana-5102	323	22	,	,	PUNCT
cana-5102	323	23	”	"	PUNCT
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cana-5102	323	25	,	,	PUNCT
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cana-5102	323	29	,	,	PUNCT
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cana-5102	324	2	,	,	PUNCT
cana-5102	324	3	feb	feb	PROPN
cana-5102	324	4	.	.	PROPN
cana-5102	324	5	2015	2015	NUM
cana-5102	324	6	,	,	PUNCT
cana-5102	324	7	doi	doi	NOUN
cana-5102	324	8	:	:	PUNCT
cana-5102	324	9	10.1109	10.1109	NUM
cana-5102	324	10	/	/	SYM
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cana-5102	324	12	.	.	PUNCT
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cana-5102	325	2	on	on	ADP
cana-5102	325	3	applied	apply	VERB
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cana-5102	325	6	issn	issn	NOUN
cana-5102	325	7	:	:	PUNCT
cana-5102	325	8	1074	1074	NUM
cana-5102	325	9	-	-	PUNCT
cana-5102	325	10	133x	133x	NUM
cana-5102	325	11	vol	vol	NOUN
cana-5102	325	12	32	32	NUM
cana-5102	325	13	no	no	NOUN
cana-5102	325	14	.	.	PUNCT
cana-5102	326	1	icmasd	icmasd	NOUN
cana-5102	326	2	(	(	PUNCT
cana-5102	326	3	2025	2025	NUM
cana-5102	326	4	)	)	PUNCT
cana-5102	326	5	770	770	NUM
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cana-5102	327	10	f.	f.	PROPN
cana-5102	327	11	kimura	kimura	PROPN
cana-5102	327	12	,	,	PUNCT
cana-5102	327	13	and	and	CCONJ
cana-5102	327	14	s.	s.	PROPN
cana-5102	327	15	pal	pal	PROPN
cana-5102	327	16	,	,	PUNCT
cana-5102	327	17	“	"	PUNCT
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cana-5102	327	19	of	of	ADP
cana-5102	327	20	off	off	ADP
cana-5102	327	21	-	-	PUNCT
cana-5102	327	22	line	line	NOUN
cana-5102	327	23	handwritten	handwritten	ADJ
cana-5102	327	24	devnagari	devnagari	ADJ
cana-5102	327	25	characters	character	NOUN
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cana-5102	327	29	,	,	PUNCT
cana-5102	327	30	”	"	PUNCT
cana-5102	327	31	pp	pp	ADV
cana-5102	327	32	.	.	PUNCT
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cana-5102	328	2	,	,	PUNCT
cana-5102	328	3	2006	2006	NUM
cana-5102	328	4	,	,	PUNCT
cana-5102	328	5	doi	doi	NOUN
cana-5102	328	6	:	:	PUNCT
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cana-5102	328	8	.	.	PUNCT
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cana-5102	329	3	]	]	PUNCT
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cana-5102	329	26	,	,	PUNCT
cana-5102	329	27	”	"	PUNCT
cana-5102	329	28	dicta	dicta	ADJ
cana-5102	329	29	2007	2007	NUM
cana-5102	329	30	,	,	PUNCT
cana-5102	329	31	pp	pp	ADP
cana-5102	329	32	.	.	PUNCT
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cana-5102	330	2	,	,	PUNCT
cana-5102	330	3	2007	2007	NUM
cana-5102	330	4	,	,	PUNCT
cana-5102	330	5	doi	doi	NOUN
cana-5102	330	6	:	:	PUNCT
cana-5102	330	7	10.1109	10.1109	NUM
cana-5102	330	8	/	/	SYM
cana-5102	330	9	dicta.2007.4426832	dicta.2007.4426832	NOUN
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cana-5102	331	15	handwritten	handwritten	ADJ
cana-5102	331	16	urdu	urdu	NOUN
cana-5102	331	17	numerals	numeral	NOUN
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cana-5102	331	30	13	13	NUM
cana-5102	331	31	,	,	PUNCT
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cana-5102	331	33	1624	1624	NUM
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cana-5102	331	41	3	3	NUM
cana-5102	331	42	,	,	PUNCT
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cana-5102	331	44	1624	1624	NUM
cana-5102	331	45	,	,	PUNCT
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cana-5102	331	48	2023	2023	NUM
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cana-5102	331	51	:	:	PUNCT
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cana-5102	331	53	/	/	SYM
cana-5102	331	54	app13031624	app13031624	NOUN
cana-5102	331	55	.	.	PUNCT
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cana-5102	332	2	31	31	NUM
cana-5102	332	3	]	]	PUNCT
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cana-5102	332	15	,	,	PUNCT
cana-5102	332	16	and	and	CCONJ
cana-5102	332	17	j.	j.	PROPN
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cana-5102	332	19	,	,	PUNCT
cana-5102	332	20	“	"	PUNCT
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cana-5102	332	23	-	-	PUNCT
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cana-5102	332	26	convolutional	convolutional	ADJ
cana-5102	332	27	neural	neural	ADJ
cana-5102	332	28	network	network	NOUN
cana-5102	332	29	for	for	ADP
cana-5102	332	30	online	online	ADJ
cana-5102	332	31	handwritten	handwritten	ADJ
cana-5102	332	32	chinese	chinese	ADJ
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cana-5102	332	34	recognition	recognition	NOUN
cana-5102	332	35	,	,	PUNCT
cana-5102	332	36	”	"	PUNCT
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cana-5102	332	38	trans	trans	PROPN
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cana-5102	332	40	netw	netw	PROPN
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cana-5102	332	42	syst	syst	NOUN
cana-5102	332	43	,	,	PUNCT
cana-5102	332	44	vol	vol	NOUN
cana-5102	332	45	.	.	PROPN
cana-5102	332	46	31	31	NUM
cana-5102	332	47	,	,	PUNCT
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cana-5102	332	50	11	11	NUM
cana-5102	332	51	,	,	PUNCT
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cana-5102	332	53	.	.	PUNCT
cana-5102	333	1	4637–4648	4637–4648	NUM
cana-5102	333	2	,	,	PUNCT
cana-5102	333	3	nov	nov	PROPN
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cana-5102	333	6	,	,	PUNCT
cana-5102	333	7	doi	doi	NOUN
cana-5102	333	8	:	:	PUNCT
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cana-5102	334	2	32	32	NUM
cana-5102	334	3	]	]	PUNCT
cana-5102	334	4	t.	t.	PROPN
cana-5102	334	5	fukumoto	fukumoto	PROPN
cana-5102	334	6	,	,	PUNCT
cana-5102	334	7	t.	t.	PROPN
cana-5102	334	8	wakabayashi	wakabayashi	PROPN
cana-5102	334	9	,	,	PUNCT
cana-5102	334	10	f.	f.	PROPN
cana-5102	334	11	kimura	kimura	PROPN
cana-5102	334	12	,	,	PUNCT
cana-5102	334	13	and	and	CCONJ
cana-5102	334	14	y.	y.	PROPN
cana-5102	334	15	miyake	miyake	PROPN
cana-5102	334	16	,	,	PUNCT
cana-5102	334	17	“	"	PUNCT
cana-5102	334	18	accuracy	accuracy	NOUN
cana-5102	334	19	improvement	improvement	NOUN
cana-5102	334	20	of	of	ADP
cana-5102	334	21	handwritten	handwritten	ADJ
cana-5102	334	22	character	character	NOUN
cana-5102	334	23	recognition	recognition	NOUN
cana-5102	334	24	by	by	ADP
cana-5102	334	25	glvq	glvq	ADJ
cana-5102	334	26	,	,	PUNCT
cana-5102	334	27	”	"	PUNCT
cana-5102	334	28	2004	2004	NUM
cana-5102	334	29	,	,	PUNCT
cana-5102	334	30	accessed	access	VERB
cana-5102	334	31	:	:	PUNCT
cana-5102	334	32	mar	mar	PROPN
cana-5102	334	33	.	.	PROPN
cana-5102	334	34	20	20	NUM
cana-5102	334	35	,	,	PUNCT
cana-5102	334	36	2023	2023	NUM
cana-5102	334	37	.	.	PUNCT
cana-5102	335	1	[	[	X
cana-5102	335	2	33	33	NUM
cana-5102	335	3	]	]	PUNCT
cana-5102	335	4	s.	s.	PROPN
cana-5102	335	5	z.	z.	PROPN
cana-5102	335	6	gurbuz	gurbuz	PROPN
cana-5102	335	7	et	et	PROPN
cana-5102	335	8	al	al	PROPN
cana-5102	335	9	.	.	PROPN
cana-5102	335	10	,	,	PUNCT
cana-5102	335	11	“	"	PUNCT
cana-5102	335	12	american	american	ADJ
cana-5102	335	13	sign	sign	NOUN
cana-5102	335	14	language	language	NOUN
cana-5102	335	15	recognition	recognition	NOUN
cana-5102	335	16	using	use	VERB
cana-5102	335	17	rf	rf	ADJ
cana-5102	335	18	sensing	sensing	NOUN
cana-5102	335	19	,	,	PUNCT
cana-5102	335	20	”	"	PUNCT
cana-5102	335	21	ieee	ieee	PROPN
cana-5102	335	22	sens	sens	PROPN
cana-5102	335	23	j	j	PROPN
cana-5102	335	24	,	,	PUNCT
cana-5102	335	25	vol	vol	NOUN
cana-5102	335	26	.	.	PROPN
cana-5102	335	27	21	21	NUM
cana-5102	335	28	,	,	PUNCT
cana-5102	335	29	no	no	INTJ
cana-5102	335	30	.	.	NOUN
cana-5102	335	31	3	3	NUM
cana-5102	335	32	,	,	PUNCT
cana-5102	335	33	pp	pp	ADJ
cana-5102	335	34	.	.	PUNCT
cana-5102	336	1	3763–3775	3763–3775	NUM
cana-5102	336	2	,	,	PUNCT
cana-5102	336	3	sep	sep	PROPN
cana-5102	336	4	.	.	PROPN
cana-5102	337	1	2020	2020	NUM
cana-5102	337	2	,	,	PUNCT
cana-5102	337	3	doi	doi	NOUN
cana-5102	337	4	:	:	PUNCT
cana-5102	337	5	10.1109	10.1109	NUM
cana-5102	337	6	/	/	SYM
cana-5102	337	7	jsen.2020.3022376	jsen.2020.3022376	PROPN
cana-5102	337	8	.	.	PUNCT
cana-5102	338	1	[	[	X
cana-5102	338	2	34	34	NUM
cana-5102	338	3	]	]	X
cana-5102	338	4	r.	r.	PROPN
cana-5102	338	5	babitha	babitha	PROPN
cana-5102	338	6	lincy	lincy	PROPN
cana-5102	338	7	,	,	PUNCT
cana-5102	338	8	j.	j.	PROPN
cana-5102	338	9	jency	jency	PROPN
cana-5102	338	10	rubia	rubia	PROPN
cana-5102	338	11	,	,	PUNCT
cana-5102	338	12	c.	c.	PROPN
cana-5102	338	13	sherin	sherin	ADJ
cana-5102	338	14	shibi	shibi	NOUN
cana-5102	338	15	,	,	PUNCT
cana-5102	338	16	and	and	CCONJ
cana-5102	338	17	m.	m.	PROPN
cana-5102	338	18	kavitha	kavitha	PROPN
cana-5102	338	19	,	,	PUNCT
cana-5102	338	20	“	"	PUNCT
cana-5102	338	21	an	an	DET
cana-5102	338	22	enhanced	enhanced	ADJ
cana-5102	338	23	deep	deep	ADJ
cana-5102	338	24	learning	learning	NOUN
cana-5102	338	25	model	model	NOUN
cana-5102	338	26	for	for	ADP
cana-5102	338	27	handwritten	handwritten	ADJ
cana-5102	338	28	tamil	tamil	PROPN
cana-5102	338	29	character	character	NOUN
cana-5102	338	30	identification	identification	NOUN
cana-5102	338	31	,	,	PUNCT
cana-5102	338	32	”	"	PUNCT
cana-5102	338	33	pp	pp	ADV
cana-5102	338	34	.	.	PUNCT
cana-5102	339	1	1518–1523	1518–1523	NUM
cana-5102	339	2	,	,	PUNCT
cana-5102	339	3	mar	mar	PROPN
cana-5102	339	4	.	.	PROPN
cana-5102	339	5	2023	2023	NUM
cana-5102	339	6	,	,	PUNCT
cana-5102	339	7	doi	doi	NOUN
cana-5102	339	8	:	:	PUNCT
cana-5102	339	9	10.1109	10.1109	NUM
cana-5102	339	10	/	/	SYM
cana-5102	339	11	icssit55814.2023.10060920	icssit55814.2023.10060920	NOUN
cana-5102	339	12	.	.	PUNCT
cana-5102	340	1	[	[	X
cana-5102	340	2	35	35	NUM
cana-5102	340	3	]	]	X
cana-5102	340	4	y.	y.	PROPN
cana-5102	340	5	yogesh	yogesh	PROPN
cana-5102	340	6	,	,	PUNCT
cana-5102	340	7	g.	g.	PROPN
cana-5102	340	8	s.	s.	PROPN
cana-5102	340	9	p.	p.	PROPN
cana-5102	340	10	ghantasala	ghantasala	PROPN
cana-5102	340	11	,	,	PUNCT
cana-5102	340	12	and	and	CCONJ
cana-5102	340	13	a.	a.	PROPN
cana-5102	340	14	priya	priya	PROPN
cana-5102	340	15	,	,	PUNCT
cana-5102	340	16	“	"	PUNCT
cana-5102	340	17	artificial	artificial	ADJ
cana-5102	340	18	intelligence	intelligence	NOUN
cana-5102	340	19	based	base	VERB
cana-5102	340	20	handwriting	handwriting	NOUN
cana-5102	340	21	digit	digit	NOUN
cana-5102	340	22	recognition	recognition	NOUN
cana-5102	340	23	(	(	PUNCT
cana-5102	340	24	hdr	hdr	NOUN
cana-5102	340	25	)	)	PUNCT
cana-5102	340	26	a	a	DET
cana-5102	340	27	technical	technical	ADJ
cana-5102	340	28	review	review	NOUN
cana-5102	340	29	,	,	PUNCT
cana-5102	340	30	”	"	PUNCT
cana-5102	340	31	proceedings	proceeding	NOUN
cana-5102	340	32	ieee	ieee	VERB
cana-5102	340	33	international	international	ADJ
cana-5102	340	34	conference	conference	NOUN
cana-5102	340	35	on	on	ADP
cana-5102	340	36	device	device	NOUN
cana-5102	340	37	intelligence	intelligence	NOUN
cana-5102	340	38	,	,	PUNCT
cana-5102	340	39	computing	computing	NOUN
cana-5102	340	40	and	and	CCONJ
cana-5102	340	41	communication	communication	NOUN
cana-5102	340	42	technologies	technology	NOUN
cana-5102	340	43	,	,	PUNCT
cana-5102	340	44	dicct	dicct	ADJ
cana-5102	340	45	2023	2023	NUM
cana-5102	340	46	,	,	PUNCT
cana-5102	340	47	pp	pp	ADJ
cana-5102	340	48	.	.	PUNCT
cana-5102	341	1	275–278	275–278	NUM
cana-5102	341	2	,	,	PUNCT
cana-5102	341	3	2023	2023	NUM
cana-5102	341	4	,	,	PUNCT
cana-5102	341	5	doi	doi	NOUN
cana-5102	341	6	:	:	PUNCT
cana-5102	341	7	10.1109	10.1109	NUM
cana-5102	341	8	/	/	SYM
cana-5102	341	9	dicct56244.2023.10110186	dicct56244.2023.10110186	PROPN
cana-5102	341	10	.	.	PUNCT