id	sid	tid	token	lemma	pos
fcis-3933	1	1	frontiers	frontier	NOUN
fcis-3933	1	2	in	in	ADP
fcis-3933	1	3	computing	computing	NOUN
fcis-3933	1	4	and	and	CCONJ
fcis-3933	1	5	intelligent	intelligent	ADJ
fcis-3933	1	6	systems	system	NOUN
fcis-3933	1	7	issn	issn	VERB
fcis-3933	1	8	:	:	PUNCT
fcis-3933	1	9	2832	2832	NUM
fcis-3933	1	10	-	-	SYM
fcis-3933	1	11	6024	6024	NUM
fcis-3933	1	12	|	|	NOUN
fcis-3933	1	13	vol	vol	NOUN
fcis-3933	1	14	.	.	PROPN
fcis-3933	2	1	2	2	NUM
fcis-3933	2	2	,	,	PUNCT
fcis-3933	2	3	no	no	INTJ
fcis-3933	2	4	.	.	NOUN
fcis-3933	2	5	2	2	NUM
fcis-3933	2	6	,	,	PUNCT
fcis-3933	2	7	2022	2022	NUM
fcis-3933	2	8	50	50	NUM
fcis-3933	2	9	research	research	NOUN
fcis-3933	2	10	on	on	ADP
fcis-3933	2	11	character	character	NOUN
fcis-3933	2	12	recognition	recognition	NOUN
fcis-3933	2	13	technology	technology	NOUN
fcis-3933	2	14	based	base	VERB
fcis-3933	2	15	on	on	ADP
fcis-3933	2	16	deep	deep	ADJ
fcis-3933	2	17	learning	learning	NOUN
fcis-3933	2	18	daodong	daodong	PROPN
fcis-3933	3	1	xiang	xiang	PROPN
fcis-3933	3	2	wenzhou	wenzhou	PROPN
fcis-3933	3	3	polytechnic	polytechnic	PROPN
fcis-3933	3	4	,	,	PUNCT
fcis-3933	3	5	wenzhou	wenzhou	PROPN
fcis-3933	3	6	,	,	PUNCT
fcis-3933	3	7	325035	325035	NUM
fcis-3933	3	8	,	,	PUNCT
fcis-3933	3	9	china	china	PROPN
fcis-3933	3	10	abstract	abstract	NOUN
fcis-3933	3	11	:	:	PUNCT
fcis-3933	3	12	in	in	ADP
fcis-3933	3	13	the	the	DET
fcis-3933	3	14	security	security	NOUN
fcis-3933	3	15	field	field	NOUN
fcis-3933	3	16	,	,	PUNCT
fcis-3933	3	17	it	it	PRON
fcis-3933	3	18	is	be	AUX
fcis-3933	3	19	necessary	necessary	ADJ
fcis-3933	3	20	to	to	PART
fcis-3933	3	21	extract	extract	VERB
fcis-3933	3	22	the	the	DET
fcis-3933	3	23	information	information	NOUN
fcis-3933	3	24	of	of	ADP
fcis-3933	3	25	each	each	DET
fcis-3933	3	26	camera	camera	NOUN
fcis-3933	3	27	in	in	ADP
fcis-3933	3	28	real	real	ADJ
fcis-3933	3	29	time	time	NOUN
fcis-3933	3	30	.	.	PUNCT
fcis-3933	4	1	however	however	ADV
fcis-3933	4	2	,	,	PUNCT
fcis-3933	4	3	due	due	ADP
fcis-3933	4	4	to	to	ADP
fcis-3933	4	5	various	various	ADJ
fcis-3933	4	6	reasons	reason	NOUN
fcis-3933	4	7	,	,	PUNCT
fcis-3933	4	8	each	each	DET
fcis-3933	4	9	camera	camera	NOUN
fcis-3933	4	10	may	may	AUX
fcis-3933	4	11	have	have	VERB
fcis-3933	4	12	wrong	wrong	ADJ
fcis-3933	4	13	shooting	shooting	NOUN
fcis-3933	4	14	time	time	NOUN
fcis-3933	4	15	,	,	PUNCT
fcis-3933	4	16	location	location	NOUN
fcis-3933	4	17	and	and	CCONJ
fcis-3933	4	18	other	other	ADJ
fcis-3933	4	19	information	information	NOUN
fcis-3933	4	20	during	during	ADP
fcis-3933	4	21	the	the	DET
fcis-3933	4	22	use	use	NOUN
fcis-3933	4	23	process	process	NOUN
fcis-3933	4	24	.	.	PUNCT
fcis-3933	5	1	if	if	SCONJ
fcis-3933	5	2	it	it	PRON
fcis-3933	5	3	can	can	AUX
fcis-3933	5	4	not	not	PART
fcis-3933	5	5	be	be	AUX
fcis-3933	5	6	found	find	VERB
fcis-3933	5	7	in	in	ADP
fcis-3933	5	8	time	time	NOUN
fcis-3933	5	9	,	,	PUNCT
fcis-3933	5	10	it	it	PRON
fcis-3933	5	11	will	will	AUX
fcis-3933	5	12	bring	bring	VERB
fcis-3933	5	13	great	great	ADJ
fcis-3933	5	14	hidden	hide	VERB
fcis-3933	5	15	danger	danger	NOUN
fcis-3933	5	16	to	to	ADP
fcis-3933	5	17	the	the	DET
fcis-3933	5	18	security	security	NOUN
fcis-3933	5	19	.	.	PUNCT
fcis-3933	6	1	this	this	DET
fcis-3933	6	2	research	research	NOUN
fcis-3933	6	3	based	base	VERB
fcis-3933	6	4	on	on	ADP
fcis-3933	6	5	crnn	crnn	ADJ
fcis-3933	6	6	depth	depth	NOUN
fcis-3933	6	7	learning	learn	VERB
fcis-3933	6	8	algorithm	algorithm	NOUN
fcis-3933	6	9	to	to	PART
fcis-3933	6	10	detect	detect	VERB
fcis-3933	6	11	and	and	CCONJ
fcis-3933	6	12	recognize	recognize	VERB
fcis-3933	6	13	the	the	DET
fcis-3933	6	14	text	text	NOUN
fcis-3933	6	15	information	information	NOUN
fcis-3933	6	16	on	on	ADP
fcis-3933	6	17	these	these	DET
fcis-3933	6	18	pictures	picture	NOUN
fcis-3933	6	19	,	,	PUNCT
fcis-3933	6	20	and	and	CCONJ
fcis-3933	6	21	the	the	DET
fcis-3933	6	22	results	result	NOUN
fcis-3933	6	23	can	can	AUX
fcis-3933	6	24	effectively	effectively	ADV
fcis-3933	6	25	improve	improve	VERB
fcis-3933	6	26	the	the	DET
fcis-3933	6	27	accuracy	accuracy	NOUN
fcis-3933	6	28	of	of	ADP
fcis-3933	6	29	recognition	recognition	NOUN
fcis-3933	6	30	.	.	PUNCT
fcis-3933	7	1	keywords	keyword	NOUN
fcis-3933	7	2	:	:	PUNCT
fcis-3933	7	3	deep	deep	ADJ
fcis-3933	7	4	learning	learning	NOUN
fcis-3933	7	5	;	;	PUNCT
fcis-3933	7	6	character	character	NOUN
fcis-3933	7	7	recognition	recognition	NOUN
fcis-3933	7	8	technology	technology	NOUN
fcis-3933	7	9	;	;	PUNCT
fcis-3933	7	10	complex	complex	ADJ
fcis-3933	7	11	background	background	NOUN
fcis-3933	7	12	.	.	PUNCT
fcis-3933	8	1	1	1	X
fcis-3933	8	2	.	.	X
fcis-3933	8	3	introduction	introduction	NOUN
fcis-3933	8	4	ocr	ocr	PROPN
fcis-3933	8	5	technology	technology	NOUN
fcis-3933	8	6	has	have	VERB
fcis-3933	8	7	a	a	DET
fcis-3933	8	8	wide	wide	ADJ
fcis-3933	8	9	range	range	NOUN
fcis-3933	8	10	of	of	ADP
fcis-3933	8	11	application	application	NOUN
fcis-3933	8	12	scenarios	scenario	NOUN
fcis-3933	8	13	.	.	PUNCT
fcis-3933	9	1	in	in	ADP
fcis-3933	9	2	many	many	ADJ
fcis-3933	9	3	scenarios	scenario	NOUN
fcis-3933	9	4	,	,	PUNCT
fcis-3933	9	5	there	there	PRON
fcis-3933	9	6	are	be	VERB
fcis-3933	9	7	requirements	requirement	NOUN
fcis-3933	9	8	for	for	ADP
fcis-3933	9	9	text	text	NOUN
fcis-3933	9	10	detection	detection	NOUN
fcis-3933	9	11	or	or	CCONJ
fcis-3933	9	12	character	character	NOUN
fcis-3933	9	13	recognition	recognition	NOUN
fcis-3933	9	14	,	,	PUNCT
fcis-3933	9	15	and	and	CCONJ
fcis-3933	9	16	there	there	PRON
fcis-3933	9	17	are	be	VERB
fcis-3933	9	18	more	more	ADV
fcis-3933	9	19	and	and	CCONJ
fcis-3933	9	20	more	more	ADJ
fcis-3933	9	21	application	application	NOUN
fcis-3933	9	22	scenarios	scenario	NOUN
fcis-3933	9	23	for	for	ADP
fcis-3933	9	24	character	character	NOUN
fcis-3933	9	25	recognition	recognition	NOUN
fcis-3933	9	26	in	in	ADP
fcis-3933	9	27	natural	natural	ADJ
fcis-3933	9	28	scenarios	scenario	NOUN
fcis-3933	9	29	.	.	PUNCT
fcis-3933	10	1	due	due	ADP
fcis-3933	10	2	to	to	ADP
fcis-3933	10	3	the	the	DET
fcis-3933	10	4	diversity	diversity	NOUN
fcis-3933	10	5	,	,	PUNCT
fcis-3933	10	6	complexity	complexity	NOUN
fcis-3933	10	7	and	and	CCONJ
fcis-3933	10	8	many	many	ADJ
fcis-3933	10	9	interference	interference	NOUN
fcis-3933	10	10	factors	factor	NOUN
fcis-3933	10	11	of	of	ADP
fcis-3933	10	12	text	text	NOUN
fcis-3933	10	13	in	in	ADP
fcis-3933	10	14	natural	natural	ADJ
fcis-3933	10	15	scenes	scene	NOUN
fcis-3933	10	16	,	,	PUNCT
fcis-3933	10	17	it	it	PRON
fcis-3933	10	18	is	be	AUX
fcis-3933	10	19	still	still	ADV
fcis-3933	10	20	difficult	difficult	ADJ
fcis-3933	10	21	to	to	PART
fcis-3933	10	22	accurately	accurately	ADV
fcis-3933	10	23	detect	detect	VERB
fcis-3933	10	24	and	and	CCONJ
fcis-3933	10	25	recognize	recognize	VERB
fcis-3933	10	26	text	text	NOUN
fcis-3933	10	27	,	,	PUNCT
fcis-3933	10	28	which	which	PRON
fcis-3933	10	29	is	be	AUX
fcis-3933	10	30	also	also	ADV
fcis-3933	10	31	a	a	DET
fcis-3933	10	32	current	current	ADJ
fcis-3933	10	33	research	research	NOUN
fcis-3933	10	34	hotspot	hotspot	NOUN
fcis-3933	10	35	.	.	PUNCT
fcis-3933	11	1	in	in	ADP
fcis-3933	11	2	the	the	DET
fcis-3933	11	3	field	field	NOUN
fcis-3933	11	4	of	of	ADP
fcis-3933	11	5	security	security	NOUN
fcis-3933	11	6	,	,	PUNCT
fcis-3933	11	7	it	it	PRON
fcis-3933	11	8	is	be	AUX
fcis-3933	11	9	necessary	necessary	ADJ
fcis-3933	11	10	to	to	PART
fcis-3933	11	11	extract	extract	VERB
fcis-3933	11	12	the	the	DET
fcis-3933	11	13	information	information	NOUN
fcis-3933	11	14	of	of	ADP
fcis-3933	11	15	each	each	DET
fcis-3933	11	16	camera	camera	NOUN
fcis-3933	11	17	in	in	ADP
fcis-3933	11	18	real	real	ADJ
fcis-3933	11	19	time	time	NOUN
fcis-3933	11	20	.	.	PUNCT
fcis-3933	12	1	however	however	ADV
fcis-3933	12	2	,	,	PUNCT
fcis-3933	12	3	due	due	ADP
fcis-3933	12	4	to	to	ADP
fcis-3933	12	5	various	various	ADJ
fcis-3933	12	6	reasons	reason	NOUN
fcis-3933	12	7	,	,	PUNCT
fcis-3933	12	8	the	the	DET
fcis-3933	12	9	camera	camera	NOUN
fcis-3933	12	10	may	may	AUX
fcis-3933	12	11	have	have	VERB
fcis-3933	12	12	wrong	wrong	ADJ
fcis-3933	12	13	shooting	shooting	NOUN
fcis-3933	12	14	time	time	NOUN
fcis-3933	12	15	,	,	PUNCT
fcis-3933	12	16	location	location	NOUN
fcis-3933	12	17	and	and	CCONJ
fcis-3933	12	18	other	other	ADJ
fcis-3933	12	19	information	information	NOUN
fcis-3933	12	20	during	during	ADP
fcis-3933	12	21	the	the	DET
fcis-3933	12	22	use	use	NOUN
fcis-3933	12	23	process	process	NOUN
fcis-3933	12	24	.	.	PUNCT
fcis-3933	13	1	if	if	SCONJ
fcis-3933	13	2	it	it	PRON
fcis-3933	13	3	can	can	AUX
fcis-3933	13	4	not	not	PART
fcis-3933	13	5	be	be	AUX
fcis-3933	13	6	found	find	VERB
fcis-3933	13	7	in	in	ADP
fcis-3933	13	8	time	time	NOUN
fcis-3933	13	9	,	,	PUNCT
fcis-3933	13	10	it	it	PRON
fcis-3933	13	11	will	will	AUX
fcis-3933	13	12	bring	bring	VERB
fcis-3933	13	13	great	great	ADJ
fcis-3933	13	14	hidden	hide	VERB
fcis-3933	13	15	dangers	danger	NOUN
fcis-3933	13	16	to	to	ADP
fcis-3933	13	17	the	the	DET
fcis-3933	13	18	security	security	NOUN
fcis-3933	13	19	.	.	PUNCT
fcis-3933	14	1	traditional	traditional	ADJ
fcis-3933	14	2	ocr	ocr	PROPN
fcis-3933	14	3	still	still	ADV
fcis-3933	14	4	stays	stay	VERB
fcis-3933	14	5	on	on	ADP
fcis-3933	14	6	image	image	NOUN
fcis-3933	14	7	processing	processing	NOUN
fcis-3933	14	8	and	and	CCONJ
fcis-3933	14	9	artificial	artificial	ADJ
fcis-3933	14	10	feature	feature	NOUN
fcis-3933	14	11	extraction	extraction	NOUN
fcis-3933	14	12	methods	method	NOUN
fcis-3933	14	13	.	.	PUNCT
fcis-3933	15	1	in	in	ADP
fcis-3933	15	2	recent	recent	ADJ
fcis-3933	15	3	years	year	NOUN
fcis-3933	15	4	,	,	PUNCT
fcis-3933	15	5	with	with	ADP
fcis-3933	15	6	the	the	DET
fcis-3933	15	7	rapid	rapid	ADJ
fcis-3933	15	8	development	development	NOUN
fcis-3933	15	9	of	of	ADP
fcis-3933	15	10	image	image	NOUN
fcis-3933	15	11	processing	processing	NOUN
fcis-3933	15	12	technology	technology	NOUN
fcis-3933	15	13	,	,	PUNCT
fcis-3933	15	14	especially	especially	ADV
fcis-3933	15	15	the	the	DET
fcis-3933	15	16	emergence	emergence	NOUN
fcis-3933	15	17	of	of	ADP
fcis-3933	15	18	deep	deep	ADJ
fcis-3933	15	19	learning	learning	NOUN
fcis-3933	15	20	and	and	CCONJ
fcis-3933	15	21	convolutional	convolutional	ADJ
fcis-3933	15	22	neural	neural	ADJ
fcis-3933	15	23	networks	network	NOUN
fcis-3933	15	24	,	,	PUNCT
fcis-3933	15	25	more	more	ADJ
fcis-3933	15	26	and	and	CCONJ
fcis-3933	15	27	more	more	ADV
fcis-3933	15	28	traditional	traditional	ADJ
fcis-3933	15	29	methods	method	NOUN
fcis-3933	15	30	have	have	AUX
fcis-3933	15	31	been	be	AUX
fcis-3933	15	32	replaced	replace	VERB
fcis-3933	15	33	by	by	ADP
fcis-3933	15	34	methods	method	NOUN
fcis-3933	15	35	based	base	VERB
fcis-3933	15	36	on	on	ADP
fcis-3933	15	37	deep	deep	ADJ
fcis-3933	15	38	learning	learning	NOUN
fcis-3933	15	39	.	.	PUNCT
fcis-3933	16	1	this	this	DET
fcis-3933	16	2	paper	paper	NOUN
fcis-3933	16	3	uses	use	VERB
fcis-3933	16	4	crnn	crnn	ADJ
fcis-3933	16	5	depth	depth	NOUN
fcis-3933	16	6	learning	learn	VERB
fcis-3933	16	7	algorithm	algorithm	NOUN
fcis-3933	16	8	to	to	PART
fcis-3933	16	9	detect	detect	VERB
fcis-3933	16	10	and	and	CCONJ
fcis-3933	16	11	recognize	recognize	VERB
fcis-3933	16	12	characters	character	NOUN
fcis-3933	16	13	on	on	ADP
fcis-3933	16	14	complex	complex	ADJ
fcis-3933	16	15	background	background	NOUN
fcis-3933	16	16	images	image	NOUN
fcis-3933	16	17	in	in	ADP
fcis-3933	16	18	specific	specific	ADJ
fcis-3933	16	19	application	application	NOUN
fcis-3933	16	20	scenarios	scenario	NOUN
fcis-3933	16	21	to	to	PART
fcis-3933	16	22	improve	improve	VERB
fcis-3933	16	23	character	character	NOUN
fcis-3933	16	24	recognition	recognition	NOUN
fcis-3933	16	25	rate	rate	NOUN
fcis-3933	16	26	.	.	PUNCT
fcis-3933	17	1	2	2	X
fcis-3933	17	2	.	.	X
fcis-3933	17	3	text	text	NOUN
fcis-3933	17	4	features	feature	NOUN
fcis-3933	17	5	in	in	ADP
fcis-3933	17	6	complex	complex	ADJ
fcis-3933	17	7	background	background	NOUN
fcis-3933	17	8	(	(	PUNCT
fcis-3933	17	9	1	1	X
fcis-3933	17	10	)	)	PUNCT
fcis-3933	17	11	diversity	diversity	NOUN
fcis-3933	17	12	of	of	ADP
fcis-3933	17	13	texts	text	NOUN
fcis-3933	17	14	different	different	ADJ
fcis-3933	17	15	from	from	ADP
fcis-3933	17	16	text	text	NOUN
fcis-3933	17	17	with	with	ADP
fcis-3933	17	18	regular	regular	ADJ
fcis-3933	17	19	font	font	NOUN
fcis-3933	17	20	and	and	CCONJ
fcis-3933	17	21	neat	neat	ADJ
fcis-3933	17	22	arrangement	arrangement	NOUN
fcis-3933	17	23	in	in	ADP
fcis-3933	17	24	document	document	NOUN
fcis-3933	17	25	images	image	NOUN
fcis-3933	17	26	,	,	PUNCT
fcis-3933	17	27	text	text	NOUN
fcis-3933	17	28	in	in	ADP
fcis-3933	17	29	complex	complex	ADJ
fcis-3933	17	30	backgrounds	background	NOUN
fcis-3933	17	31	may	may	AUX
fcis-3933	17	32	have	have	VERB
fcis-3933	17	33	completely	completely	ADV
fcis-3933	17	34	different	different	ADJ
fcis-3933	17	35	fonts	font	NOUN
fcis-3933	17	36	,	,	PUNCT
fcis-3933	17	37	colors	color	NOUN
fcis-3933	17	38	,	,	PUNCT
fcis-3933	17	39	proportions	proportion	NOUN
fcis-3933	17	40	and	and	CCONJ
fcis-3933	17	41	directions	direction	NOUN
fcis-3933	17	42	even	even	ADV
fcis-3933	17	43	in	in	ADP
fcis-3933	17	44	the	the	DET
fcis-3933	17	45	same	same	ADJ
fcis-3933	17	46	scene	scene	NOUN
fcis-3933	17	47	.	.	PUNCT
fcis-3933	18	1	(	(	PUNCT
fcis-3933	18	2	2	2	X
fcis-3933	18	3	)	)	PUNCT
fcis-3933	18	4	complexity	complexity	NOUN
fcis-3933	18	5	of	of	ADP
fcis-3933	18	6	the	the	DET
fcis-3933	18	7	background	background	NOUN
fcis-3933	18	8	in	in	ADP
fcis-3933	18	9	natural	natural	ADJ
fcis-3933	18	10	scenes	scene	NOUN
fcis-3933	18	11	,	,	PUNCT
fcis-3933	18	12	the	the	DET
fcis-3933	18	13	background	background	NOUN
fcis-3933	18	14	in	in	ADP
fcis-3933	18	15	images	image	NOUN
fcis-3933	18	16	and	and	CCONJ
fcis-3933	18	17	videos	video	NOUN
fcis-3933	18	18	can	can	AUX
fcis-3933	18	19	be	be	AUX
fcis-3933	18	20	very	very	ADV
fcis-3933	18	21	complex	complex	ADJ
fcis-3933	18	22	.	.	PUNCT
fcis-3933	19	1	elements	element	NOUN
fcis-3933	19	2	such	such	ADJ
fcis-3933	19	3	as	as	ADP
fcis-3933	19	4	logos	logo	NOUN
fcis-3933	19	5	and	and	CCONJ
fcis-3933	19	6	tags	tag	NOUN
fcis-3933	19	7	are	be	AUX
fcis-3933	19	8	actually	actually	ADV
fcis-3933	19	9	indistinguishable	indistinguishable	ADJ
fcis-3933	19	10	from	from	ADP
fcis-3933	19	11	real	real	ADJ
fcis-3933	19	12	text	text	NOUN
fcis-3933	19	13	,	,	PUNCT
fcis-3933	19	14	so	so	CCONJ
fcis-3933	19	15	it	it	PRON
fcis-3933	19	16	is	be	AUX
fcis-3933	19	17	easy	easy	ADJ
fcis-3933	19	18	to	to	PART
fcis-3933	19	19	cause	cause	VERB
fcis-3933	19	20	confusion	confusion	NOUN
fcis-3933	19	21	and	and	CCONJ
fcis-3933	19	22	errors	error	NOUN
fcis-3933	19	23	.	.	PUNCT
fcis-3933	20	1	(	(	PUNCT
fcis-3933	20	2	3	3	X
fcis-3933	20	3	)	)	PUNCT
fcis-3933	20	4	many	many	ADJ
fcis-3933	20	5	interferences	interference	NOUN
fcis-3933	20	6	factor	factor	NOUN
fcis-3933	20	7	various	various	ADJ
fcis-3933	20	8	interference	interference	NOUN
fcis-3933	20	9	factors	factor	NOUN
fcis-3933	20	10	,	,	PUNCT
fcis-3933	20	11	such	such	ADJ
fcis-3933	20	12	as	as	ADP
fcis-3933	20	13	noise	noise	NOUN
fcis-3933	20	14	,	,	PUNCT
fcis-3933	20	15	blurring	blurring	NOUN
fcis-3933	20	16	,	,	PUNCT
fcis-3933	20	17	distortion	distortion	NOUN
fcis-3933	20	18	,	,	PUNCT
fcis-3933	20	19	low	low	ADJ
fcis-3933	20	20	resolution	resolution	NOUN
fcis-3933	20	21	,	,	PUNCT
fcis-3933	20	22	uneven	uneven	ADJ
fcis-3933	20	23	illumination	illumination	NOUN
fcis-3933	20	24	and	and	CCONJ
fcis-3933	20	25	partial	partial	ADJ
fcis-3933	20	26	occlusion	occlusion	NOUN
fcis-3933	20	27	,	,	PUNCT
fcis-3933	20	28	may	may	AUX
fcis-3933	20	29	lead	lead	VERB
fcis-3933	20	30	to	to	ADP
fcis-3933	20	31	the	the	DET
fcis-3933	20	32	failure	failure	NOUN
fcis-3933	20	33	of	of	ADP
fcis-3933	20	34	text	text	NOUN
fcis-3933	20	35	detection	detection	NOUN
fcis-3933	20	36	and	and	CCONJ
fcis-3933	20	37	recognition	recognition	NOUN
fcis-3933	20	38	.	.	PUNCT
fcis-3933	21	1	3	3	X
fcis-3933	21	2	.	.	X
fcis-3933	21	3	deep	deep	ADJ
fcis-3933	21	4	learning	learning	NOUN
fcis-3933	21	5	method	method	PROPN
fcis-3933	21	6	ocr	ocr	ADJ
fcis-3933	21	7	identification	identification	NOUN
fcis-3933	21	8	process	process	NOUN
fcis-3933	21	9	:	:	PUNCT
fcis-3933	21	10	ocr	ocr	ADJ
fcis-3933	21	11	recognition	recognition	NOUN
fcis-3933	21	12	preprocessing	preprocessing	NOUN
fcis-3933	21	13	:	:	PUNCT
fcis-3933	21	14	graying	gray	VERB
fcis-3933	21	15	(	(	PUNCT
fcis-3933	21	16	if	if	SCONJ
fcis-3933	21	17	it	it	PRON
fcis-3933	21	18	is	be	AUX
fcis-3933	21	19	a	a	DET
fcis-3933	21	20	color	color	NOUN
fcis-3933	21	21	image	image	NOUN
fcis-3933	21	22	)	)	PUNCT
fcis-3933	21	23	,	,	PUNCT
fcis-3933	21	24	noise	noise	NOUN
fcis-3933	21	25	reduction	reduction	NOUN
fcis-3933	21	26	,	,	PUNCT
fcis-3933	21	27	binarization	binarization	NOUN
fcis-3933	21	28	,	,	PUNCT
fcis-3933	21	29	character	character	NOUN
fcis-3933	21	30	segmentation	segmentation	NOUN
fcis-3933	21	31	and	and	CCONJ
fcis-3933	21	32	normalization	normalization	NOUN
fcis-3933	21	33	.	.	PUNCT
fcis-3933	22	1	after	after	ADP
fcis-3933	22	2	binarization	binarization	NOUN
fcis-3933	22	3	,	,	PUNCT
fcis-3933	22	4	there	there	PRON
fcis-3933	22	5	are	be	VERB
fcis-3933	22	6	only	only	ADV
fcis-3933	22	7	two	two	NUM
fcis-3933	22	8	colors	color	NOUN
fcis-3933	22	9	left	leave	VERB
fcis-3933	22	10	in	in	ADP
fcis-3933	22	11	the	the	DET
fcis-3933	22	12	image	image	NOUN
fcis-3933	22	13	,	,	PUNCT
fcis-3933	22	14	namely	namely	ADV
fcis-3933	22	15	black	black	ADJ
fcis-3933	22	16	and	and	CCONJ
fcis-3933	22	17	white	white	ADJ
fcis-3933	22	18	.	.	PUNCT
fcis-3933	23	1	one	one	NUM
fcis-3933	23	2	is	be	AUX
fcis-3933	23	3	the	the	DET
fcis-3933	23	4	image	image	NOUN
fcis-3933	23	5	background	background	NOUN
fcis-3933	23	6	,	,	PUNCT
fcis-3933	23	7	and	and	CCONJ
fcis-3933	23	8	the	the	DET
fcis-3933	23	9	other	other	ADJ
fcis-3933	23	10	is	be	AUX
fcis-3933	23	11	the	the	DET
fcis-3933	23	12	text	text	NOUN
fcis-3933	23	13	to	to	PART
fcis-3933	23	14	be	be	AUX
fcis-3933	23	15	recognized	recognize	VERB
fcis-3933	23	16	;	;	PUNCT
fcis-3933	23	17	noise	noise	NOUN
fcis-3933	23	18	reduction	reduction	NOUN
fcis-3933	23	19	is	be	AUX
fcis-3933	23	20	very	very	ADV
fcis-3933	23	21	important	important	ADJ
fcis-3933	23	22	at	at	ADP
fcis-3933	23	23	this	this	DET
fcis-3933	23	24	stage	stage	NOUN
fcis-3933	23	25	,	,	PUNCT
fcis-3933	23	26	and	and	CCONJ
fcis-3933	23	27	the	the	DET
fcis-3933	23	28	quality	quality	NOUN
fcis-3933	23	29	of	of	ADP
fcis-3933	23	30	noise	noise	NOUN
fcis-3933	23	31	reduction	reduction	NOUN
fcis-3933	23	32	algorithm	algorithm	NOUN
fcis-3933	23	33	has	have	VERB
fcis-3933	23	34	a	a	DET
fcis-3933	23	35	great	great	ADJ
fcis-3933	23	36	impact	impact	NOUN
fcis-3933	23	37	on	on	ADP
fcis-3933	23	38	feature	feature	NOUN
fcis-3933	23	39	extraction	extraction	NOUN
fcis-3933	23	40	.	.	PUNCT
fcis-3933	24	1	character	character	NOUN
fcis-3933	24	2	segmentation	segmentation	NOUN
fcis-3933	24	3	is	be	AUX
fcis-3933	24	4	to	to	PART
fcis-3933	24	5	divide	divide	VERB
fcis-3933	24	6	the	the	DET
fcis-3933	24	7	characters	character	NOUN
fcis-3933	24	8	in	in	ADP
fcis-3933	24	9	the	the	DET
fcis-3933	24	10	image	image	NOUN
fcis-3933	24	11	into	into	ADP
fcis-3933	24	12	single	single	ADJ
fcis-3933	24	13	characters	character	NOUN
fcis-3933	24	14	one	one	NUM
fcis-3933	24	15	character	character	NOUN
fcis-3933	24	16	at	at	ADP
fcis-3933	24	17	a	a	DET
fcis-3933	24	18	time	time	NOUN
fcis-3933	24	19	.	.	PUNCT
fcis-3933	25	1	if	if	SCONJ
fcis-3933	25	2	the	the	DET
fcis-3933	25	3	text	text	NOUN
fcis-3933	25	4	line	line	NOUN
fcis-3933	25	5	is	be	AUX
fcis-3933	25	6	slanted	slant	VERB
fcis-3933	25	7	,	,	PUNCT
fcis-3933	25	8	it	it	PRON
fcis-3933	25	9	is	be	AUX
fcis-3933	25	10	often	often	ADV
fcis-3933	25	11	necessary	necessary	ADJ
fcis-3933	25	12	to	to	PART
fcis-3933	25	13	correct	correct	VERB
fcis-3933	25	14	the	the	DET
fcis-3933	25	15	slant	slant	NOUN
fcis-3933	25	16	.	.	PUNCT
fcis-3933	26	1	normalization	normalization	NOUN
fcis-3933	26	2	is	be	AUX
fcis-3933	26	3	to	to	PART
fcis-3933	26	4	normalize	normalize	VERB
fcis-3933	26	5	the	the	DET
fcis-3933	26	6	text	text	NOUN
fcis-3933	26	7	image	image	NOUN
fcis-3933	26	8	of	of	ADP
fcis-3933	26	9	a	a	DET
fcis-3933	26	10	single	single	ADJ
fcis-3933	26	11	medium	medium	NOUN
fcis-3933	26	12	to	to	ADP
fcis-3933	26	13	the	the	DET
fcis-3933	26	14	same	same	ADJ
fcis-3933	26	15	ruler	ruler	NOUN
fcis-3933	26	16	.	.	PUNCT
fcis-3933	27	1	(	(	PUNCT
fcis-3933	27	2	2	2	X
fcis-3933	27	3	)	)	PUNCT
fcis-3933	27	4	feature	feature	NOUN
fcis-3933	27	5	extraction	extraction	NOUN
fcis-3933	27	6	and	and	CCONJ
fcis-3933	27	7	dimension	dimension	NOUN
fcis-3933	27	8	reduction	reduction	NOUN
fcis-3933	27	9	:	:	PUNCT
fcis-3933	27	10	features	feature	NOUN
fcis-3933	27	11	are	be	AUX
fcis-3933	27	12	the	the	DET
fcis-3933	27	13	key	key	ADJ
fcis-3933	27	14	information	information	NOUN
fcis-3933	27	15	used	use	VERB
fcis-3933	27	16	to	to	PART
fcis-3933	27	17	identify	identify	VERB
fcis-3933	27	18	characters	character	NOUN
fcis-3933	27	19	,	,	PUNCT
fcis-3933	27	20	and	and	CCONJ
fcis-3933	27	21	each	each	DET
fcis-3933	27	22	different	different	ADJ
fcis-3933	27	23	character	character	NOUN
fcis-3933	27	24	can	can	AUX
fcis-3933	27	25	be	be	AUX
fcis-3933	27	26	distinguished	distinguish	VERB
fcis-3933	27	27	from	from	ADP
fcis-3933	27	28	other	other	ADJ
fcis-3933	27	29	characters	character	NOUN
fcis-3933	27	30	through	through	ADP
fcis-3933	27	31	features	feature	NOUN
fcis-3933	27	32	.	.	PUNCT
fcis-3933	28	1	for	for	ADP
fcis-3933	28	2	numbers	number	NOUN
fcis-3933	28	3	and	and	CCONJ
fcis-3933	28	4	english	english	ADJ
fcis-3933	28	5	letters	letter	NOUN
fcis-3933	28	6	,	,	PUNCT
fcis-3933	28	7	this	this	DET
fcis-3933	28	8	feature	feature	NOUN
fcis-3933	28	9	extraction	extraction	NOUN
fcis-3933	28	10	is	be	AUX
fcis-3933	28	11	relatively	relatively	ADV
fcis-3933	28	12	easy	easy	ADJ
fcis-3933	28	13	,	,	PUNCT
fcis-3933	28	14	because	because	SCONJ
fcis-3933	28	15	there	there	PRON
fcis-3933	28	16	are	be	VERB
fcis-3933	28	17	only	only	ADV
fcis-3933	28	18	10	10	NUM
fcis-3933	28	19	numbers	number	NOUN
fcis-3933	28	20	and	and	CCONJ
fcis-3933	28	21	52	52	NUM
fcis-3933	28	22	english	english	ADJ
fcis-3933	28	23	letters	letter	NOUN
fcis-3933	28	24	,	,	PUNCT
fcis-3933	28	25	all	all	PRON
fcis-3933	28	26	of	of	ADP
fcis-3933	28	27	which	which	PRON
fcis-3933	28	28	are	be	AUX
fcis-3933	28	29	small	small	ADJ
fcis-3933	28	30	character	character	NOUN
fcis-3933	28	31	sets	set	NOUN
fcis-3933	28	32	.	.	PUNCT
fcis-3933	29	1	for	for	ADP
fcis-3933	29	2	chinese	chinese	ADJ
fcis-3933	29	3	characters	character	NOUN
fcis-3933	29	4	,	,	PUNCT
fcis-3933	29	5	feature	feature	NOUN
fcis-3933	29	6	extraction	extraction	NOUN
fcis-3933	29	7	is	be	AUX
fcis-3933	29	8	difficult	difficult	ADJ
fcis-3933	29	9	,	,	PUNCT
fcis-3933	29	10	because	because	SCONJ
fcis-3933	29	11	first	first	ADV
fcis-3933	29	12	of	of	ADP
fcis-3933	29	13	all	all	PRON
fcis-3933	29	14	,	,	PUNCT
fcis-3933	29	15	chinese	chinese	ADJ
fcis-3933	29	16	characters	character	NOUN
fcis-3933	29	17	are	be	AUX
fcis-3933	29	18	large	large	ADJ
fcis-3933	29	19	character	character	NOUN
fcis-3933	29	20	sets	set	NOUN
fcis-3933	29	21	.	.	PUNCT
fcis-3933	30	1	in	in	ADP
fcis-3933	30	2	the	the	DET
fcis-3933	30	3	national	national	ADJ
fcis-3933	30	4	standard	standard	NOUN
fcis-3933	30	5	,	,	PUNCT
fcis-3933	30	6	there	there	PRON
fcis-3933	30	7	are	be	VERB
fcis-3933	30	8	3755	3755	NUM
fcis-3933	30	9	first	first	ADJ
fcis-3933	30	10	level	level	NOUN
fcis-3933	30	11	chinese	chinese	ADJ
fcis-3933	30	12	characters	character	NOUN
fcis-3933	30	13	that	that	PRON
fcis-3933	30	14	are	be	AUX
fcis-3933	30	15	most	most	ADV
fcis-3933	30	16	commonly	commonly	ADV
fcis-3933	30	17	used	use	VERB
fcis-3933	30	18	;	;	PUNCT
fcis-3933	30	19	the	the	DET
fcis-3933	30	20	second	second	ADJ
fcis-3933	30	21	chinese	chinese	ADJ
fcis-3933	30	22	character	character	NOUN
fcis-3933	30	23	has	have	VERB
fcis-3933	30	24	a	a	DET
fcis-3933	30	25	complex	complex	ADJ
fcis-3933	30	26	structure	structure	NOUN
fcis-3933	30	27	and	and	CCONJ
fcis-3933	30	28	many	many	ADJ
fcis-3933	30	29	similar	similar	ADJ
fcis-3933	30	30	characters	character	NOUN
fcis-3933	30	31	.	.	PUNCT
fcis-3933	31	1	after	after	ADP
fcis-3933	31	2	determining	determine	VERB
fcis-3933	31	3	which	which	DET
fcis-3933	31	4	feature	feature	NOUN
fcis-3933	31	5	to	to	PART
fcis-3933	31	6	use	use	VERB
fcis-3933	31	7	,	,	PUNCT
fcis-3933	31	8	depending	depend	VERB
fcis-3933	31	9	on	on	ADP
fcis-3933	31	10	the	the	DET
fcis-3933	31	11	situation	situation	NOUN
fcis-3933	31	12	,	,	PUNCT
fcis-3933	31	13	it	it	PRON
fcis-3933	31	14	is	be	AUX
fcis-3933	31	15	also	also	ADV
fcis-3933	31	16	possible	possible	ADJ
fcis-3933	31	17	to	to	PART
fcis-3933	31	18	reduce	reduce	VERB
fcis-3933	31	19	the	the	DET
fcis-3933	31	20	dimension	dimension	NOUN
fcis-3933	31	21	of	of	ADP
fcis-3933	31	22	the	the	DET
fcis-3933	31	23	feature	feature	NOUN
fcis-3933	31	24	.	.	PUNCT
fcis-3933	32	1	this	this	DET
fcis-3933	32	2	situation	situation	NOUN
fcis-3933	32	3	is	be	AUX
fcis-3933	32	4	that	that	SCONJ
fcis-3933	32	5	if	if	SCONJ
fcis-3933	32	6	the	the	DET
fcis-3933	32	7	dimension	dimension	NOUN
fcis-3933	32	8	of	of	ADP
fcis-3933	32	9	the	the	DET
fcis-3933	32	10	feature	feature	NOUN
fcis-3933	32	11	is	be	AUX
fcis-3933	32	12	too	too	ADV
fcis-3933	32	13	high	high	ADJ
fcis-3933	32	14	,	,	PUNCT
fcis-3933	32	15	the	the	DET
fcis-3933	32	16	efficiency	efficiency	NOUN
fcis-3933	32	17	of	of	ADP
fcis-3933	32	18	the	the	DET
fcis-3933	32	19	classifier	classifier	NOUN
fcis-3933	32	20	will	will	AUX
fcis-3933	32	21	be	be	AUX
fcis-3933	32	22	greatly	greatly	ADV
fcis-3933	32	23	affected	affect	VERB
fcis-3933	32	24	.	.	PUNCT
fcis-3933	33	1	in	in	ADP
fcis-3933	33	2	order	order	NOUN
fcis-3933	33	3	to	to	PART
fcis-3933	33	4	improve	improve	VERB
fcis-3933	33	5	the	the	DET
fcis-3933	33	6	recognition	recognition	NOUN
fcis-3933	33	7	rate	rate	NOUN
fcis-3933	33	8	,	,	PUNCT
fcis-3933	33	9	it	it	PRON
fcis-3933	33	10	is	be	AUX
fcis-3933	33	11	often	often	ADV
fcis-3933	33	12	necessary	necessary	ADJ
fcis-3933	33	13	to	to	PART
fcis-3933	33	14	reduce	reduce	VERB
fcis-3933	33	15	the	the	DET
fcis-3933	33	16	dimension	dimension	NOUN
fcis-3933	33	17	.	.	PUNCT
fcis-3933	34	1	this	this	DET
fcis-3933	34	2	process	process	NOUN
fcis-3933	34	3	is	be	AUX
fcis-3933	34	4	also	also	ADV
fcis-3933	34	5	very	very	ADV
fcis-3933	34	6	important	important	ADJ
fcis-3933	34	7	.	.	PUNCT
fcis-3933	35	1	it	it	PRON
fcis-3933	35	2	is	be	AUX
fcis-3933	35	3	necessary	necessary	ADJ
fcis-3933	35	4	to	to	PART
fcis-3933	35	5	reduce	reduce	VERB
fcis-3933	35	6	the	the	DET
fcis-3933	35	7	dimension	dimension	NOUN
fcis-3933	35	8	and	and	CCONJ
fcis-3933	35	9	make	make	VERB
fcis-3933	35	10	the	the	DET
fcis-3933	35	11	feature	feature	NOUN
fcis-3933	35	12	vector	vector	NOUN
fcis-3933	35	13	after	after	ADP
fcis-3933	35	14	reducing	reduce	VERB
fcis-3933	35	15	the	the	DET
fcis-3933	35	16	dimension	dimension	NOUN
fcis-3933	35	17	retain	retain	VERB
fcis-3933	35	18	enough	enough	ADJ
fcis-3933	35	19	information	information	NOUN
fcis-3933	35	20	.	.	PUNCT
fcis-3933	36	1	(	(	PUNCT
fcis-3933	36	2	3	3	X
fcis-3933	36	3	)	)	PUNCT
fcis-3933	36	4	classifier	classifier	NOUN
fcis-3933	36	5	design	design	NOUN
fcis-3933	36	6	,	,	PUNCT
fcis-3933	36	7	training	training	NOUN
fcis-3933	36	8	and	and	CCONJ
fcis-3933	36	9	actual	actual	ADJ
fcis-3933	36	10	recognition	recognition	NOUN
fcis-3933	36	11	:	:	PUNCT
fcis-3933	36	12	the	the	DET
fcis-3933	36	13	classifier	classifier	NOUN
fcis-3933	36	14	is	be	AUX
fcis-3933	36	15	used	use	VERB
fcis-3933	36	16	for	for	ADP
fcis-3933	36	17	recognition	recognition	NOUN
fcis-3933	36	18	,	,	PUNCT
fcis-3933	36	19	that	that	ADV
fcis-3933	36	20	is	is	ADV
fcis-3933	36	21	,	,	PUNCT
fcis-3933	36	22	for	for	ADP
fcis-3933	36	23	the	the	DET
fcis-3933	36	24	second	second	ADJ
fcis-3933	36	25	step	step	NOUN
fcis-3933	36	26	,	,	PUNCT
fcis-3933	36	27	extract	extract	VERB
fcis-3933	36	28	features	feature	NOUN
fcis-3933	36	29	from	from	ADP
fcis-3933	36	30	a	a	DET
fcis-3933	36	31	text	text	NOUN
fcis-3933	36	32	image	image	NOUN
fcis-3933	36	33	to	to	ADP
fcis-3933	36	34	the	the	DET
fcis-3933	36	35	classifier	classifier	NOUN
fcis-3933	36	36	,	,	PUNCT
fcis-3933	36	37	and	and	CCONJ
fcis-3933	36	38	the	the	DET
fcis-3933	36	39	classifier	classifier	NOUN
fcis-3933	36	40	will	will	AUX
fcis-3933	36	41	classify	classify	VERB
fcis-3933	36	42	it	it	PRON
fcis-3933	36	43	to	to	PART
fcis-3933	36	44	tell	tell	VERB
fcis-3933	36	45	you	you	PRON
fcis-3933	36	46	which	which	DET
fcis-3933	36	47	text	text	NOUN
fcis-3933	36	48	this	this	DET
fcis-3933	36	49	feature	feature	NOUN
fcis-3933	36	50	should	should	AUX
fcis-3933	36	51	be	be	AUX
fcis-3933	36	52	recognized	recognize	VERB
fcis-3933	36	53	.	.	PUNCT
fcis-3933	37	1	(	(	PUNCT
fcis-3933	37	2	4	4	X
fcis-3933	37	3	)	)	PUNCT
fcis-3933	37	4	ocr	ocr	ADJ
fcis-3933	37	5	recognition	recognition	NOUN
fcis-3933	37	6	post	post	ADJ
fcis-3933	37	7	-	-	ADJ
fcis-3933	37	8	processing	processing	ADJ
fcis-3933	37	9	:	:	PUNCT
fcis-3933	37	10	post	post	ADJ
fcis-3933	37	11	-	-	ADJ
fcis-3933	37	12	processing	processing	NOUN
fcis-3933	37	13	is	be	AUX
fcis-3933	37	14	used	use	VERB
fcis-3933	37	15	to	to	PART
fcis-3933	37	16	optimize	optimize	VERB
fcis-3933	37	17	the	the	DET
fcis-3933	37	18	classification	classification	NOUN
fcis-3933	37	19	results	result	NOUN
fcis-3933	37	20	.	.	PUNCT
fcis-3933	38	1	first	first	ADV
fcis-3933	38	2	,	,	PUNCT
fcis-3933	38	3	sometimes	sometimes	ADV
fcis-3933	38	4	the	the	DET
fcis-3933	38	5	classification	classification	NOUN
fcis-3933	38	6	of	of	ADP
fcis-3933	38	7	the	the	DET
fcis-3933	38	8	classifier	classifier	NOUN
fcis-3933	38	9	is	be	AUX
fcis-3933	38	10	not	not	PART
fcis-3933	38	11	completely	completely	ADV
fcis-3933	38	12	correct	correct	ADJ
fcis-3933	38	13	.	.	PUNCT
fcis-3933	39	1	for	for	ADP
fcis-3933	39	2	example	example	NOUN
fcis-3933	39	3	,	,	PUNCT
fcis-3933	39	4	for	for	ADP
fcis-3933	39	5	the	the	DET
fcis-3933	39	6	recognition	recognition	NOUN
fcis-3933	39	7	of	of	ADP
fcis-3933	39	8	chinese	chinese	ADJ
fcis-3933	39	9	characters	character	NOUN
fcis-3933	39	10	,	,	PUNCT
fcis-3933	39	11	it	it	PRON
fcis-3933	39	12	is	be	AUX
fcis-3933	39	13	easy	easy	ADJ
fcis-3933	39	14	to	to	PART
fcis-3933	39	15	recognize	recognize	VERB
fcis-3933	39	16	a	a	DET
fcis-3933	39	17	character	character	NOUN
fcis-3933	39	18	as	as	ADP
fcis-3933	39	19	a	a	DET
fcis-3933	39	20	similar	similar	ADJ
fcis-3933	39	21	character	character	NOUN
fcis-3933	39	22	because	because	SCONJ
fcis-3933	39	23	of	of	ADP
fcis-3933	39	24	51	51	NUM
fcis-3933	39	25	the	the	DET
fcis-3933	39	26	existence	existence	NOUN
fcis-3933	39	27	of	of	ADP
fcis-3933	39	28	similar	similar	ADJ
fcis-3933	39	29	characters	character	NOUN
fcis-3933	39	30	in	in	ADP
fcis-3933	39	31	chinese	chinese	ADJ
fcis-3933	39	32	characters	character	NOUN
fcis-3933	39	33	.	.	PUNCT
fcis-3933	40	1	this	this	DET
fcis-3933	40	2	problem	problem	NOUN
fcis-3933	40	3	can	can	AUX
fcis-3933	40	4	be	be	AUX
fcis-3933	40	5	solved	solve	VERB
fcis-3933	40	6	in	in	ADP
fcis-3933	40	7	post	post	ADJ
fcis-3933	40	8	-	-	ADJ
fcis-3933	40	9	processing	processing	NOUN
fcis-3933	40	10	,	,	PUNCT
fcis-3933	40	11	for	for	ADP
fcis-3933	40	12	example	example	NOUN
fcis-3933	40	13	,	,	PUNCT
fcis-3933	40	14	the	the	DET
fcis-3933	40	15	language	language	NOUN
fcis-3933	40	16	model	model	NOUN
fcis-3933	40	17	is	be	AUX
fcis-3933	40	18	used	use	VERB
fcis-3933	40	19	for	for	ADP
fcis-3933	40	20	correction	correction	NOUN
fcis-3933	40	21	.	.	PUNCT
fcis-3933	41	1	if	if	SCONJ
fcis-3933	41	2	the	the	DET
fcis-3933	41	3	classifier	classifier	NOUN
fcis-3933	41	4	identifies	identify	VERB
fcis-3933	41	5	"	"	PUNCT
fcis-3933	41	6	where	where	SCONJ
fcis-3933	41	7	"	"	PUNCT
fcis-3933	41	8	as	as	ADP
fcis-3933	41	9	"	"	PUNCT
fcis-3933	41	10	where	where	SCONJ
fcis-3933	41	11	to	to	PART
fcis-3933	41	12	store	store	VERB
fcis-3933	41	13	"	"	PUNCT
fcis-3933	41	14	,	,	PUNCT
fcis-3933	41	15	it	it	PRON
fcis-3933	41	16	will	will	AUX
fcis-3933	41	17	find	find	VERB
fcis-3933	41	18	that	that	SCONJ
fcis-3933	41	19	"	"	PUNCT
fcis-3933	41	20	where	where	SCONJ
fcis-3933	41	21	to	to	PART
fcis-3933	41	22	store	store	VERB
fcis-3933	41	23	"	"	PUNCT
fcis-3933	41	24	is	be	AUX
fcis-3933	41	25	wrong	wrong	ADJ
fcis-3933	41	26	through	through	ADP
fcis-3933	41	27	the	the	DET
fcis-3933	41	28	language	language	NOUN
fcis-3933	41	29	model	model	NOUN
fcis-3933	41	30	,	,	PUNCT
fcis-3933	41	31	and	and	CCONJ
fcis-3933	41	32	then	then	ADV
fcis-3933	41	33	correct	correct	VERB
fcis-3933	41	34	it	it	PRON
fcis-3933	41	35	.	.	PUNCT
fcis-3933	42	1	second	second	ADV
fcis-3933	42	2	,	,	PUNCT
fcis-3933	42	3	ocr	ocr	NOUN
fcis-3933	42	4	recognizes	recognize	VERB
fcis-3933	42	5	images	image	NOUN
fcis-3933	42	6	that	that	PRON
fcis-3933	42	7	often	often	ADV
fcis-3933	42	8	have	have	VERB
fcis-3933	42	9	a	a	DET
fcis-3933	42	10	large	large	ADJ
fcis-3933	42	11	number	number	NOUN
fcis-3933	42	12	of	of	ADP
fcis-3933	42	13	characters	character	NOUN
fcis-3933	42	14	,	,	PUNCT
fcis-3933	42	15	and	and	CCONJ
fcis-3933	42	16	these	these	DET
fcis-3933	42	17	characters	character	NOUN
fcis-3933	42	18	are	be	AUX
fcis-3933	42	19	stored	store	VERB
fcis-3933	42	20	in	in	ADP
fcis-3933	42	21	complex	complex	ADJ
fcis-3933	42	22	situations	situation	NOUN
fcis-3933	42	23	such	such	ADJ
fcis-3933	42	24	as	as	ADP
fcis-3933	42	25	typesetting	typeset	VERB
fcis-3933	42	26	and	and	CCONJ
fcis-3933	42	27	font	font	VERB
fcis-3933	42	28	size	size	NOUN
fcis-3933	42	29	.	.	PUNCT
fcis-3933	43	1	in	in	ADP
fcis-3933	43	2	postprocessing	postprocesse	VERB
fcis-3933	43	3	,	,	PUNCT
fcis-3933	43	4	you	you	PRON
fcis-3933	43	5	can	can	AUX
fcis-3933	43	6	try	try	VERB
fcis-3933	43	7	to	to	PART
fcis-3933	43	8	format	format	VERB
fcis-3933	43	9	the	the	DET
fcis-3933	43	10	recognition	recognition	NOUN
fcis-3933	43	11	results	result	VERB
fcis-3933	43	12	,	,	PUNCT
fcis-3933	43	13	such	such	ADJ
fcis-3933	43	14	as	as	ADP
fcis-3933	43	15	arranging	arrange	VERB
fcis-3933	43	16	them	they	PRON
fcis-3933	43	17	according	accord	VERB
fcis-3933	43	18	to	to	ADP
fcis-3933	43	19	the	the	DET
fcis-3933	43	20	typesetting	typesetting	NOUN
fcis-3933	43	21	in	in	ADP
fcis-3933	43	22	the	the	DET
fcis-3933	43	23	image	image	NOUN
fcis-3933	43	24	.	.	PUNCT
fcis-3933	44	1	the	the	DET
fcis-3933	44	2	system	system	NOUN
fcis-3933	44	3	flow	flow	NOUN
fcis-3933	44	4	of	of	ADP
fcis-3933	44	5	traditional	traditional	ADJ
fcis-3933	44	6	ocr	ocr	ADJ
fcis-3933	44	7	technology	technology	NOUN
fcis-3933	44	8	is	be	AUX
fcis-3933	44	9	basically	basically	ADV
fcis-3933	44	10	shown	show	VERB
fcis-3933	44	11	in	in	ADP
fcis-3933	44	12	the	the	DET
fcis-3933	44	13	figure	figure	NOUN
fcis-3933	44	14	1	1	NUM
fcis-3933	44	15	.	.	PUNCT
fcis-3933	44	16	figure	figure	NOUN
fcis-3933	44	17	1	1	NUM
fcis-3933	44	18	.	.	PUNCT
fcis-3933	44	19	traditional	traditional	ADJ
fcis-3933	44	20	ocr	ocr	ADJ
fcis-3933	44	21	technology	technology	NOUN
fcis-3933	44	22	with	with	ADP
fcis-3933	44	23	the	the	DET
fcis-3933	44	24	continuous	continuous	ADJ
fcis-3933	44	25	development	development	NOUN
fcis-3933	44	26	of	of	ADP
fcis-3933	44	27	deep	deep	ADJ
fcis-3933	44	28	learning	learning	NOUN
fcis-3933	44	29	technology	technology	NOUN
fcis-3933	44	30	,	,	PUNCT
fcis-3933	44	31	the	the	DET
fcis-3933	44	32	traditional	traditional	ADJ
fcis-3933	44	33	ocr	ocr	ADJ
fcis-3933	44	34	technology	technology	NOUN
fcis-3933	44	35	framework	framework	NOUN
fcis-3933	44	36	is	be	AUX
fcis-3933	44	37	ignored	ignore	VERB
fcis-3933	44	38	.	.	PUNCT
fcis-3933	45	1	in	in	ADP
fcis-3933	45	2	the	the	DET
fcis-3933	45	3	field	field	NOUN
fcis-3933	45	4	of	of	ADP
fcis-3933	45	5	computer	computer	NOUN
fcis-3933	45	6	vision	vision	NOUN
fcis-3933	45	7	,	,	PUNCT
fcis-3933	45	8	convolutional	convolutional	ADJ
fcis-3933	45	9	neural	neural	ADJ
fcis-3933	45	10	networks	network	NOUN
fcis-3933	45	11	are	be	AUX
fcis-3933	45	12	used	use	VERB
fcis-3933	45	13	to	to	PART
fcis-3933	45	14	do	do	VERB
fcis-3933	45	15	research	research	NOUN
fcis-3933	45	16	on	on	ADP
fcis-3933	45	17	ocr	ocr	PROPN
fcis-3933	45	18	technology	technology	NOUN
fcis-3933	45	19	,	,	PUNCT
fcis-3933	45	20	and	and	CCONJ
fcis-3933	45	21	the	the	DET
fcis-3933	45	22	recognition	recognition	NOUN
fcis-3933	45	23	rate	rate	NOUN
fcis-3933	45	24	is	be	AUX
fcis-3933	45	25	very	very	ADV
fcis-3933	45	26	high	high	ADJ
fcis-3933	45	27	,	,	PUNCT
fcis-3933	45	28	the	the	DET
fcis-3933	45	29	effect	effect	NOUN
fcis-3933	45	30	is	be	AUX
fcis-3933	45	31	very	very	ADV
fcis-3933	45	32	good	good	ADJ
fcis-3933	45	33	.	.	PUNCT
fcis-3933	46	1	you	you	PRON
fcis-3933	46	2	do	do	AUX
fcis-3933	46	3	n't	not	PART
fcis-3933	46	4	need	need	VERB
fcis-3933	46	5	to	to	PART
fcis-3933	46	6	spend	spend	VERB
fcis-3933	46	7	a	a	DET
fcis-3933	46	8	lot	lot	NOUN
fcis-3933	46	9	of	of	ADP
fcis-3933	46	10	time	time	NOUN
fcis-3933	46	11	designing	design	VERB
fcis-3933	46	12	character	character	NOUN
fcis-3933	46	13	features	feature	NOUN
fcis-3933	46	14	.	.	PUNCT
fcis-3933	47	1	in	in	ADP
fcis-3933	47	2	ocr	ocr	ADJ
fcis-3933	47	3	system	system	NOUN
fcis-3933	47	4	based	base	VERB
fcis-3933	47	5	on	on	ADP
fcis-3933	47	6	deep	deep	ADJ
fcis-3933	47	7	learning	learning	NOUN
fcis-3933	47	8	,	,	PUNCT
fcis-3933	47	9	neural	neural	ADJ
fcis-3933	47	10	network	network	NOUN
fcis-3933	47	11	model	model	NOUN
fcis-3933	47	12	is	be	AUX
fcis-3933	47	13	used	use	VERB
fcis-3933	47	14	as	as	ADP
fcis-3933	47	15	feature	feature	NOUN
fcis-3933	47	16	extractor	extractor	NOUN
fcis-3933	47	17	and	and	CCONJ
fcis-3933	47	18	classifier	classifier	NOUN
fcis-3933	47	19	.	.	PUNCT
fcis-3933	48	1	the	the	DET
fcis-3933	48	2	input	input	NOUN
fcis-3933	48	3	is	be	AUX
fcis-3933	48	4	character	character	NOUN
fcis-3933	48	5	image	image	NOUN
fcis-3933	48	6	,	,	PUNCT
fcis-3933	48	7	and	and	CCONJ
fcis-3933	48	8	the	the	DET
fcis-3933	48	9	output	output	NOUN
fcis-3933	48	10	is	be	AUX
fcis-3933	48	11	recognition	recognition	NOUN
fcis-3933	48	12	result	result	NOUN
fcis-3933	48	13	,	,	PUNCT
fcis-3933	48	14	so	so	CCONJ
fcis-3933	48	15	the	the	DET
fcis-3933	48	16	whole	whole	ADJ
fcis-3933	48	17	workflow	workflow	NOUN
fcis-3933	48	18	becomes	become	VERB
fcis-3933	48	19	very	very	ADV
fcis-3933	48	20	simple	simple	ADJ
fcis-3933	48	21	.	.	PUNCT
fcis-3933	49	1	the	the	DET
fcis-3933	49	2	typical	typical	ADJ
fcis-3933	49	3	neural	neural	ADJ
fcis-3933	49	4	network	network	NOUN
fcis-3933	49	5	structure	structure	NOUN
fcis-3933	49	6	is	be	AUX
fcis-3933	49	7	shown	show	VERB
fcis-3933	49	8	in	in	ADP
fcis-3933	49	9	figure	figure	NOUN
fcis-3933	49	10	2	2	NUM
fcis-3933	49	11	.	.	PUNCT
fcis-3933	49	12	figure	figure	NOUN
fcis-3933	49	13	2	2	NUM
fcis-3933	49	14	.	.	PUNCT
fcis-3933	49	15	typical	typical	ADJ
fcis-3933	49	16	neural	neural	ADJ
fcis-3933	49	17	network	network	NOUN
fcis-3933	49	18	structure	structure	NOUN
fcis-3933	49	19	neural	neural	ADJ
fcis-3933	49	20	network	network	NOUN
fcis-3933	49	21	(	(	PUNCT
fcis-3933	49	22	nns	nns	PROPN
fcis-3933	49	23	)	)	PUNCT
fcis-3933	49	24	is	be	AUX
fcis-3933	49	25	an	an	DET
fcis-3933	49	26	algorithm	algorithm	NOUN
fcis-3933	49	27	mathematical	mathematical	ADJ
fcis-3933	49	28	model	model	NOUN
fcis-3933	49	29	that	that	PRON
fcis-3933	49	30	imitates	imitate	VERB
fcis-3933	49	31	the	the	DET
fcis-3933	49	32	behavior	behavior	NOUN
fcis-3933	49	33	characteristics	characteristic	NOUN
fcis-3933	49	34	of	of	ADP
fcis-3933	49	35	animal	animal	NOUN
fcis-3933	49	36	neural	neural	ADJ
fcis-3933	49	37	networks	network	NOUN
fcis-3933	49	38	and	and	CCONJ
fcis-3933	49	39	conducts	conduct	NOUN
fcis-3933	49	40	distributed	distribute	VERB
fcis-3933	49	41	parallel	parallel	ADJ
fcis-3933	49	42	information	information	NOUN
fcis-3933	49	43	processing	processing	NOUN
fcis-3933	49	44	.	.	PUNCT
fcis-3933	50	1	this	this	DET
fcis-3933	50	2	kind	kind	NOUN
fcis-3933	50	3	of	of	ADP
fcis-3933	50	4	network	network	NOUN
fcis-3933	50	5	relies	rely	VERB
fcis-3933	50	6	on	on	ADP
fcis-3933	50	7	the	the	DET
fcis-3933	50	8	complexity	complexity	NOUN
fcis-3933	50	9	of	of	ADP
fcis-3933	50	10	the	the	DET
fcis-3933	50	11	system	system	NOUN
fcis-3933	50	12	,	,	PUNCT
fcis-3933	50	13	and	and	CCONJ
fcis-3933	50	14	achieves	achieve	VERB
fcis-3933	50	15	the	the	DET
fcis-3933	50	16	purpose	purpose	NOUN
fcis-3933	50	17	of	of	ADP
fcis-3933	50	18	processing	process	VERB
fcis-3933	50	19	information	information	NOUN
fcis-3933	50	20	by	by	ADP
fcis-3933	50	21	adjusting	adjust	VERB
fcis-3933	50	22	the	the	DET
fcis-3933	50	23	interconnection	interconnection	NOUN
fcis-3933	50	24	between	between	ADP
fcis-3933	50	25	a	a	DET
fcis-3933	50	26	large	large	ADJ
fcis-3933	50	27	number	number	NOUN
fcis-3933	50	28	of	of	ADP
fcis-3933	50	29	internal	internal	ADJ
fcis-3933	50	30	nodes	node	NOUN
fcis-3933	50	31	.	.	PUNCT
fcis-3933	51	1	the	the	DET
fcis-3933	51	2	purpose	purpose	NOUN
fcis-3933	51	3	of	of	ADP
fcis-3933	51	4	neural	neural	ADJ
fcis-3933	51	5	network	network	NOUN
fcis-3933	51	6	design	design	NOUN
fcis-3933	51	7	is	be	AUX
fcis-3933	51	8	to	to	PART
fcis-3933	51	9	try	try	VERB
fcis-3933	51	10	to	to	PART
fcis-3933	51	11	write	write	VERB
fcis-3933	51	12	a	a	DET
fcis-3933	51	13	general	general	ADJ
fcis-3933	51	14	network	network	NOUN
fcis-3933	51	15	model	model	NOUN
fcis-3933	51	16	,	,	PUNCT
fcis-3933	51	17	and	and	CCONJ
fcis-3933	51	18	then	then	ADV
fcis-3933	51	19	train	train	VERB
fcis-3933	51	20	against	against	ADP
fcis-3933	51	21	the	the	DET
fcis-3933	51	22	data	datum	NOUN
fcis-3933	51	23	,	,	PUNCT
fcis-3933	51	24	so	so	SCONJ
fcis-3933	51	25	as	as	SCONJ
fcis-3933	51	26	to	to	PART
fcis-3933	51	27	continuously	continuously	ADV
fcis-3933	51	28	improve	improve	VERB
fcis-3933	51	29	the	the	DET
fcis-3933	51	30	parameters	parameter	NOUN
fcis-3933	51	31	in	in	ADP
fcis-3933	51	32	the	the	DET
fcis-3933	51	33	model	model	NOUN
fcis-3933	51	34	,	,	PUNCT
fcis-3933	51	35	and	and	CCONJ
fcis-3933	51	36	finally	finally	ADV
fcis-3933	51	37	achieve	achieve	VERB
fcis-3933	51	38	the	the	DET
fcis-3933	51	39	output	output	NOUN
fcis-3933	51	40	results	result	NOUN
fcis-3933	51	41	meet	meet	VERB
fcis-3933	51	42	the	the	DET
fcis-3933	51	43	expectations	expectation	NOUN
fcis-3933	51	44	.	.	PUNCT
fcis-3933	52	1	common	common	ADJ
fcis-3933	52	2	neural	neural	ADJ
fcis-3933	52	3	networks	network	NOUN
fcis-3933	52	4	are	be	AUX
fcis-3933	52	5	as	as	SCONJ
fcis-3933	52	6	follows	follow	VERB
fcis-3933	52	7	:	:	PUNCT
fcis-3933	52	8	feedforward	feedforward	NOUN
fcis-3933	52	9	neural	neural	ADJ
fcis-3933	52	10	network	network	NOUN
fcis-3933	52	11	a	a	DET
fcis-3933	52	12	unidirectional	unidirectional	ADJ
fcis-3933	52	13	multilayer	multilayer	ADJ
fcis-3933	52	14	structure	structure	NOUN
fcis-3933	52	15	is	be	AUX
fcis-3933	52	16	adopted	adopt	VERB
fcis-3933	52	17	.	.	PUNCT
fcis-3933	53	1	each	each	DET
fcis-3933	53	2	layer	layer	NOUN
fcis-3933	53	3	contains	contain	VERB
fcis-3933	53	4	several	several	ADJ
fcis-3933	53	5	neurons	neuron	NOUN
fcis-3933	53	6	.	.	PUNCT
fcis-3933	54	1	the	the	DET
fcis-3933	54	2	neurons	neuron	NOUN
fcis-3933	54	3	in	in	ADP
fcis-3933	54	4	the	the	DET
fcis-3933	54	5	same	same	ADJ
fcis-3933	54	6	layer	layer	NOUN
fcis-3933	54	7	are	be	AUX
fcis-3933	54	8	not	not	PART
fcis-3933	54	9	connected	connect	VERB
fcis-3933	54	10	with	with	ADP
fcis-3933	54	11	each	each	DET
fcis-3933	54	12	other	other	ADJ
fcis-3933	54	13	,	,	PUNCT
fcis-3933	54	14	and	and	CCONJ
fcis-3933	54	15	the	the	DET
fcis-3933	54	16	information	information	NOUN
fcis-3933	54	17	between	between	ADP
fcis-3933	54	18	layers	layer	NOUN
fcis-3933	54	19	is	be	AUX
fcis-3933	54	20	transmitted	transmit	VERB
fcis-3933	54	21	only	only	ADV
fcis-3933	54	22	in	in	ADP
fcis-3933	54	23	one	one	NUM
fcis-3933	54	24	direction	direction	NOUN
fcis-3933	54	25	.	.	PUNCT
fcis-3933	55	1	the	the	DET
fcis-3933	55	2	first	first	ADJ
fcis-3933	55	3	layer	layer	NOUN
fcis-3933	55	4	is	be	AUX
fcis-3933	55	5	called	call	VERB
fcis-3933	55	6	the	the	DET
fcis-3933	55	7	input	input	NOUN
fcis-3933	55	8	layer	layer	NOUN
fcis-3933	55	9	.	.	PUNCT
fcis-3933	56	1	the	the	DET
fcis-3933	56	2	last	last	ADJ
fcis-3933	56	3	layer	layer	NOUN
fcis-3933	56	4	is	be	AUX
fcis-3933	56	5	the	the	DET
fcis-3933	56	6	output	output	NOUN
fcis-3933	56	7	layer	layer	NOUN
fcis-3933	56	8	.	.	PUNCT
fcis-3933	57	1	the	the	DET
fcis-3933	57	2	middle	middle	ADJ
fcis-3933	57	3	layer	layer	NOUN
fcis-3933	57	4	is	be	AUX
fcis-3933	57	5	the	the	DET
fcis-3933	57	6	hidden	hide	VERB
fcis-3933	57	7	layer	layer	NOUN
fcis-3933	57	8	,	,	PUNCT
fcis-3933	57	9	referred	refer	VERB
fcis-3933	57	10	to	to	ADP
fcis-3933	57	11	as	as	ADP
fcis-3933	57	12	the	the	DET
fcis-3933	57	13	hidden	hide	VERB
fcis-3933	57	14	layer	layer	NOUN
fcis-3933	57	15	.	.	PUNCT
fcis-3933	58	1	the	the	DET
fcis-3933	58	2	hidden	hide	VERB
fcis-3933	58	3	layer	layer	NOUN
fcis-3933	58	4	can	can	AUX
fcis-3933	58	5	be	be	AUX
fcis-3933	58	6	one	one	NUM
fcis-3933	58	7	layer	layer	NOUN
fcis-3933	58	8	.	.	PUNCT
fcis-3933	59	1	it	it	PRON
fcis-3933	59	2	can	can	AUX
fcis-3933	59	3	also	also	ADV
fcis-3933	59	4	be	be	AUX
fcis-3933	59	5	multi	multi	ADJ
fcis-3933	59	6	-	-	NOUN
fcis-3933	59	7	layer	layer	NOUN
fcis-3933	59	8	.	.	PUNCT
fcis-3933	60	1	convolution	convolution	NOUN
fcis-3933	60	2	neural	neural	ADJ
fcis-3933	60	3	network	network	NOUN
fcis-3933	60	4	:	:	PUNCT
fcis-3933	60	5	the	the	DET
fcis-3933	60	6	convolutional	convolutional	ADJ
fcis-3933	60	7	neural	neural	ADJ
fcis-3933	60	8	network	network	NOUN
fcis-3933	60	9	is	be	AUX
fcis-3933	60	10	composed	compose	VERB
fcis-3933	60	11	of	of	ADP
fcis-3933	60	12	one	one	NUM
fcis-3933	60	13	or	or	CCONJ
fcis-3933	60	14	more	more	ADJ
fcis-3933	60	15	convolution	convolution	NOUN
fcis-3933	60	16	layers	layer	NOUN
fcis-3933	60	17	and	and	CCONJ
fcis-3933	60	18	the	the	DET
fcis-3933	60	19	top	top	ADJ
fcis-3933	60	20	full	full	ADJ
fcis-3933	60	21	connection	connection	NOUN
fcis-3933	60	22	layer	layer	NOUN
fcis-3933	60	23	(	(	PUNCT
fcis-3933	60	24	corresponding	correspond	VERB
fcis-3933	60	25	to	to	ADP
fcis-3933	60	26	the	the	DET
fcis-3933	60	27	classical	classical	ADJ
fcis-3933	60	28	neural	neural	ADJ
fcis-3933	60	29	network	network	NOUN
fcis-3933	60	30	)	)	PUNCT
fcis-3933	60	31	,	,	PUNCT
fcis-3933	60	32	as	as	ADV
fcis-3933	60	33	well	well	ADV
fcis-3933	60	34	as	as	ADP
fcis-3933	60	35	the	the	DET
fcis-3933	60	36	associated	associated	ADJ
fcis-3933	60	37	weight	weight	NOUN
fcis-3933	60	38	and	and	CCONJ
fcis-3933	60	39	pooling	pool	VERB
fcis-3933	60	40	layer	layer	NOUN
fcis-3933	60	41	.	.	PUNCT
fcis-3933	61	1	this	this	DET
fcis-3933	61	2	structure	structure	NOUN
fcis-3933	61	3	enables	enable	VERB
fcis-3933	61	4	the	the	DET
fcis-3933	61	5	convolutional	convolutional	ADJ
fcis-3933	61	6	neural	neural	ADJ
fcis-3933	61	7	network	network	NOUN
fcis-3933	61	8	to	to	PART
fcis-3933	61	9	use	use	VERB
fcis-3933	61	10	the	the	DET
fcis-3933	61	11	two	two	NUM
fcis-3933	61	12	-	-	PUNCT
fcis-3933	61	13	dimensional	dimensional	ADJ
fcis-3933	61	14	structure	structure	NOUN
fcis-3933	61	15	of	of	ADP
fcis-3933	61	16	input	input	NOUN
fcis-3933	61	17	data	datum	NOUN
fcis-3933	61	18	.	.	PUNCT
fcis-3933	62	1	compared	compare	VERB
fcis-3933	62	2	with	with	ADP
fcis-3933	62	3	other	other	ADJ
fcis-3933	62	4	deep	deep	ADJ
fcis-3933	62	5	learning	learning	NOUN
fcis-3933	62	6	structures	structure	NOUN
fcis-3933	62	7	,	,	PUNCT
fcis-3933	62	8	convolutional	convolutional	ADJ
fcis-3933	62	9	neural	neural	ADJ
fcis-3933	62	10	network	network	NOUN
fcis-3933	62	11	can	can	AUX
fcis-3933	62	12	give	give	VERB
fcis-3933	62	13	better	well	ADJ
fcis-3933	62	14	results	result	NOUN
fcis-3933	62	15	in	in	ADP
fcis-3933	62	16	image	image	NOUN
fcis-3933	62	17	and	and	CCONJ
fcis-3933	62	18	speech	speech	NOUN
fcis-3933	62	19	recognition	recognition	NOUN
fcis-3933	62	20	.	.	PUNCT
fcis-3933	63	1	cyclic	cyclic	ADJ
fcis-3933	63	2	neural	neural	ADJ
fcis-3933	63	3	network	network	NOUN
fcis-3933	63	4	rnn	rnn	NOUN
fcis-3933	63	5	is	be	AUX
fcis-3933	63	6	a	a	DET
fcis-3933	63	7	neural	neural	ADJ
fcis-3933	63	8	network	network	NOUN
fcis-3933	63	9	used	use	VERB
fcis-3933	63	10	to	to	PART
fcis-3933	63	11	process	process	VERB
fcis-3933	63	12	sequence	sequence	NOUN
fcis-3933	63	13	data	datum	NOUN
fcis-3933	63	14	.	.	PUNCT
fcis-3933	64	1	it	it	PRON
fcis-3933	64	2	introduces	introduce	VERB
fcis-3933	64	3	structural	structural	ADJ
fcis-3933	64	4	units	unit	NOUN
fcis-3933	64	5	with	with	ADP
fcis-3933	64	6	"	"	PUNCT
fcis-3933	64	7	memory	memory	NOUN
fcis-3933	64	8	"	"	PUNCT
fcis-3933	64	9	property	property	NOUN
fcis-3933	64	10	.	.	PUNCT
fcis-3933	65	1	besides	besides	SCONJ
fcis-3933	65	2	this	this	DET
fcis-3933	65	3	input	input	NOUN
fcis-3933	65	4	,	,	PUNCT
fcis-3933	65	5	the	the	DET
fcis-3933	65	6	calculation	calculation	NOUN
fcis-3933	65	7	also	also	ADV
fcis-3933	65	8	includes	include	VERB
fcis-3933	65	9	the	the	DET
fcis-3933	65	10	last	last	ADJ
fcis-3933	65	11	calculation	calculation	NOUN
fcis-3933	65	12	result	result	NOUN
fcis-3933	65	13	.	.	PUNCT
fcis-3933	66	1	4	4	X
fcis-3933	66	2	.	.	X
fcis-3933	66	3	crnn	crnn	PROPN
fcis-3933	66	4	model	model	PROPN
fcis-3933	66	5	structure	structure	NOUN
fcis-3933	66	6	crnn	crnn	NOUN
fcis-3933	66	7	is	be	AUX
fcis-3933	66	8	a	a	DET
fcis-3933	66	9	convolutional	convolutional	ADJ
fcis-3933	66	10	recurrent	recurrent	ADJ
fcis-3933	66	11	neural	neural	ADJ
fcis-3933	66	12	network	network	NOUN
fcis-3933	66	13	structure	structure	NOUN
fcis-3933	66	14	,	,	PUNCT
fcis-3933	66	15	which	which	PRON
fcis-3933	66	16	is	be	AUX
fcis-3933	66	17	used	use	VERB
fcis-3933	66	18	to	to	PART
fcis-3933	66	19	solve	solve	VERB
fcis-3933	66	20	image	image	NOUN
fcis-3933	66	21	-	-	PUNCT
fcis-3933	66	22	based	base	VERB
fcis-3933	66	23	sequence	sequence	NOUN
fcis-3933	66	24	recognition	recognition	NOUN
fcis-3933	66	25	problems	problem	NOUN
fcis-3933	66	26	,	,	PUNCT
fcis-3933	66	27	especially	especially	ADV
fcis-3933	66	28	scene	scene	NOUN
fcis-3933	66	29	character	character	NOUN
fcis-3933	66	30	recognition	recognition	NOUN
fcis-3933	66	31	problems	problem	NOUN
fcis-3933	66	32	.	.	PUNCT
fcis-3933	67	1	crnn	crnn	PROPN
fcis-3933	67	2	network	network	NOUN
fcis-3933	67	3	realizes	realize	VERB
fcis-3933	67	4	indefinite	indefinite	ADJ
fcis-3933	67	5	length	length	NOUN
fcis-3933	67	6	verification	verification	NOUN
fcis-3933	67	7	,	,	PUNCT
fcis-3933	67	8	combines	combine	VERB
fcis-3933	67	9	cnn	cnn	PROPN
fcis-3933	67	10	and	and	CCONJ
fcis-3933	67	11	rnn	rnn	PROPN
fcis-3933	67	12	network	network	NOUN
fcis-3933	67	13	structures	structure	NOUN
fcis-3933	67	14	,	,	PUNCT
fcis-3933	67	15	uses	use	VERB
fcis-3933	67	16	bidirectional	bidirectional	ADJ
fcis-3933	67	17	lstm	lstm	ADJ
fcis-3933	67	18	cyclic	cyclic	NOUN
fcis-3933	67	19	network	network	NOUN
fcis-3933	67	20	for	for	ADP
fcis-3933	67	21	timing	time	VERB
fcis-3933	67	22	training	training	NOUN
fcis-3933	67	23	,	,	PUNCT
fcis-3933	67	24	and	and	CCONJ
fcis-3933	67	25	finally	finally	ADV
fcis-3933	67	26	introduces	introduce	VERB
fcis-3933	67	27	ctc	ctc	NOUN
fcis-3933	67	28	loss	loss	NOUN
fcis-3933	67	29	function	function	NOUN
fcis-3933	67	30	to	to	PART
fcis-3933	67	31	realize	realize	VERB
fcis-3933	67	32	end	end	NOUN
fcis-3933	67	33	-	-	PUNCT
fcis-3933	67	34	to	to	ADP
fcis-3933	67	35	-	-	PUNCT
fcis-3933	67	36	end	end	NOUN
fcis-3933	67	37	indefinite	indefinite	ADJ
fcis-3933	67	38	length	length	NOUN
fcis-3933	67	39	sequence	sequence	NOUN
fcis-3933	67	40	recognition	recognition	NOUN
fcis-3933	67	41	the	the	DET
fcis-3933	67	42	network	network	NOUN
fcis-3933	67	43	structure	structure	NOUN
fcis-3933	67	44	consists	consist	VERB
fcis-3933	67	45	of	of	ADP
fcis-3933	67	46	three	three	NUM
fcis-3933	67	47	parts	part	NOUN
fcis-3933	67	48	,	,	PUNCT
fcis-3933	67	49	from	from	ADP
fcis-3933	67	50	bottom	bottom	NOUN
fcis-3933	67	51	to	to	ADP
fcis-3933	67	52	top	top	NOUN
fcis-3933	67	53	as	as	ADP
fcis-3933	67	54	figure	figure	NOUN
fcis-3933	67	55	3	3	NUM
fcis-3933	67	56	.	.	PUNCT
fcis-3933	67	57	(	(	PUNCT
fcis-3933	67	58	1	1	X
fcis-3933	67	59	)	)	PUNCT
fcis-3933	67	60	convolutional	convolutional	ADJ
fcis-3933	67	61	layer	layer	NOUN
fcis-3933	67	62	.	.	PUNCT
fcis-3933	68	1	the	the	DET
fcis-3933	68	2	function	function	NOUN
fcis-3933	68	3	is	be	AUX
fcis-3933	68	4	to	to	PART
fcis-3933	68	5	extract	extract	VERB
fcis-3933	68	6	feature	feature	NOUN
fcis-3933	68	7	sequences	sequence	NOUN
fcis-3933	68	8	from	from	ADP
fcis-3933	68	9	input	input	NOUN
fcis-3933	68	10	images	image	NOUN
fcis-3933	68	11	.	.	PUNCT
fcis-3933	69	1	(	(	PUNCT
fcis-3933	69	2	2	2	X
fcis-3933	69	3	)	)	PUNCT
fcis-3933	69	4	cycle	cycle	NOUN
fcis-3933	69	5	layer	layer	NOUN
fcis-3933	69	6	.	.	PUNCT
fcis-3933	70	1	the	the	DET
fcis-3933	70	2	function	function	NOUN
fcis-3933	70	3	is	be	AUX
fcis-3933	70	4	to	to	PART
fcis-3933	70	5	predict	predict	VERB
fcis-3933	70	6	the	the	DET
fcis-3933	70	7	label	label	NOUN
fcis-3933	70	8	(	(	PUNCT
fcis-3933	70	9	real	real	ADJ
fcis-3933	70	10	value	value	NOUN
fcis-3933	70	11	)	)	PUNCT
fcis-3933	70	12	distribution	distribution	NOUN
fcis-3933	70	13	of	of	ADP
fcis-3933	70	14	the	the	DET
fcis-3933	70	15	feature	feature	NOUN
fcis-3933	70	16	sequence	sequence	NOUN
fcis-3933	70	17	obtained	obtain	VERB
fcis-3933	70	18	from	from	ADP
fcis-3933	70	19	the	the	DET
fcis-3933	70	20	convolution	convolution	NOUN
fcis-3933	70	21	layer	layer	NOUN
fcis-3933	70	22	.	.	PUNCT
fcis-3933	71	1	(	(	PUNCT
fcis-3933	71	2	3	3	X
fcis-3933	71	3	)	)	PUNCT
fcis-3933	71	4	transcription	transcription	NOUN
fcis-3933	71	5	layer	layer	NOUN
fcis-3933	71	6	.	.	PUNCT
fcis-3933	72	1	the	the	DET
fcis-3933	72	2	function	function	NOUN
fcis-3933	72	3	is	be	AUX
fcis-3933	72	4	to	to	PART
fcis-3933	72	5	convert	convert	VERB
fcis-3933	72	6	the	the	DET
fcis-3933	72	7	label	label	NOUN
fcis-3933	72	8	distribution	distribution	NOUN
fcis-3933	72	9	obtained	obtain	VERB
fcis-3933	72	10	from	from	ADP
fcis-3933	72	11	the	the	DET
fcis-3933	72	12	loop	loop	NOUN
fcis-3933	72	13	layer	layer	NOUN
fcis-3933	72	14	into	into	ADP
fcis-3933	72	15	the	the	DET
fcis-3933	72	16	final	final	ADJ
fcis-3933	72	17	recognition	recognition	NOUN
fcis-3933	72	18	result	result	VERB
fcis-3933	72	19	through	through	ADP
fcis-3933	72	20	de	de	ADJ
fcis-3933	72	21	integration	integration	NOUN
fcis-3933	72	22	and	and	CCONJ
fcis-3933	72	23	other	other	ADJ
fcis-3933	72	24	operations	operation	NOUN
fcis-3933	72	25	.	.	PUNCT
fcis-3933	73	1	figure	figure	VERB
fcis-3933	73	2	3	3	NUM
fcis-3933	73	3	.	.	PUNCT
fcis-3933	74	1	crnn	crnn	PROPN
fcis-3933	74	2	network	network	NOUN
fcis-3933	74	3	structure	structure	NOUN
fcis-3933	74	4	from	from	ADP
fcis-3933	74	5	:	:	PUNCT
fcis-3933	74	6	https://zhuanlan.zhihu.com/p/43534801	https://zhuanlan.zhihu.com/p/43534801	PROPN
fcis-3933	74	7	5	5	NUM
fcis-3933	74	8	.	.	PUNCT
fcis-3933	74	9	experimental	experimental	ADJ
fcis-3933	74	10	process	process	NOUN
fcis-3933	74	11	1)collection	1)collection	NUM
fcis-3933	74	12	of	of	ADP
fcis-3933	74	13	data	datum	NOUN
fcis-3933	74	14	sets	set	VERB
fcis-3933	74	15	the	the	DET
fcis-3933	74	16	pictures	picture	NOUN
fcis-3933	74	17	in	in	ADP
fcis-3933	74	18	this	this	DET
fcis-3933	74	19	study	study	NOUN
fcis-3933	74	20	are	be	AUX
fcis-3933	74	21	from	from	ADP
fcis-3933	74	22	real	real	ADJ
fcis-3933	74	23	-	-	PUNCT
fcis-3933	74	24	time	time	NOUN
fcis-3933	74	25	photos	photo	NOUN
fcis-3933	74	26	taken	take	VERB
fcis-3933	74	27	by	by	ADP
fcis-3933	74	28	cameras	camera	NOUN
fcis-3933	74	29	installed	instal	VERB
fcis-3933	74	30	at	at	ADP
fcis-3933	74	31	different	different	ADJ
fcis-3933	74	32	points	point	NOUN
fcis-3933	74	33	.	.	PUNCT
fcis-3933	75	1	through	through	ADP
fcis-3933	75	2	sorting	sort	VERB
fcis-3933	75	3	out	out	ADP
fcis-3933	75	4	the	the	DET
fcis-3933	75	5	collected	collect	VERB
fcis-3933	75	6	data	datum	NOUN
fcis-3933	75	7	,	,	PUNCT
fcis-3933	75	8	we	we	PRON
fcis-3933	75	9	found	find	VERB
fcis-3933	75	10	that	that	SCONJ
fcis-3933	75	11	the	the	DET
fcis-3933	75	12	most	most	ADV
fcis-3933	75	13	prone	prone	ADJ
fcis-3933	75	14	to	to	ADP
fcis-3933	75	15	errors	error	NOUN
fcis-3933	75	16	were	be	AUX
fcis-3933	75	17	the	the	DET
fcis-3933	75	18	irregular	irregular	ADJ
fcis-3933	75	19	arrangement	arrangement	NOUN
fcis-3933	75	20	of	of	ADP
fcis-3933	75	21	characters	character	NOUN
fcis-3933	75	22	and	and	CCONJ
fcis-3933	75	23	the	the	DET
fcis-3933	75	24	lack	lack	NOUN
fcis-3933	75	25	of	of	ADP
fcis-3933	75	26	characters	character	NOUN
fcis-3933	75	27	.	.	PUNCT
fcis-3933	76	1	2)dataset	2)dataset	NUM
fcis-3933	76	2	extension	extension	NOUN
fcis-3933	76	3	preprocess	preprocess	NOUN
fcis-3933	76	4	the	the	DET
fcis-3933	76	5	real	real	ADJ
fcis-3933	76	6	background	background	NOUN
fcis-3933	76	7	texture	texture	NOUN
fcis-3933	76	8	material	material	NOUN
fcis-3933	76	9	.	.	PUNCT
fcis-3933	77	1	for	for	ADP
fcis-3933	77	2	image	image	NOUN
fcis-3933	77	3	data	datum	NOUN
fcis-3933	77	4	,	,	PUNCT
fcis-3933	77	5	expand	expand	VERB
fcis-3933	77	6	the	the	DET
fcis-3933	77	7	training	training	NOUN
fcis-3933	77	8	data	datum	NOUN
fcis-3933	77	9	by	by	ADP
fcis-3933	77	10	rotating	rotate	VERB
fcis-3933	77	11	,	,	PUNCT
fcis-3933	77	12	converting	converting	NOUN
fcis-3933	77	13	,	,	PUNCT
fcis-3933	77	14	and	and	CCONJ
fcis-3933	77	15	distorting	distort	VERB
fcis-3933	77	16	the	the	DET
fcis-3933	77	17	image	image	NOUN
fcis-3933	77	18	,	,	PUNCT
fcis-3933	77	19	and	and	CCONJ
fcis-3933	77	20	train	train	VERB
fcis-3933	77	21	the	the	DET
fcis-3933	77	22	model	model	NOUN
fcis-3933	77	23	on	on	ADP
fcis-3933	77	24	the	the	DET
fcis-3933	77	25	extended	extend	VERB
fcis-3933	77	26	data	data	NOUN
fcis-3933	77	27	set	set	VERB
fcis-3933	77	28	.	.	PUNCT
fcis-3933	78	1	3)model	3)model	NUM
fcis-3933	78	2	construction	construction	NOUN
fcis-3933	78	3	build	build	VERB
fcis-3933	78	4	a	a	DET
fcis-3933	78	5	basic	basic	ADJ
fcis-3933	78	6	cnn	cnn	PROPN
fcis-3933	78	7	network	network	NOUN
fcis-3933	78	8	.	.	PUNCT
fcis-3933	79	1	in	in	ADP
fcis-3933	79	2	the	the	DET
fcis-3933	79	3	training	training	NOUN
fcis-3933	79	4	process	process	NOUN
fcis-3933	79	5	,	,	PUNCT
fcis-3933	79	6	first	first	ADV
fcis-3933	79	7	y	y	PROPN
fcis-3933	79	8	1	1	NUM
fcis-3933	79	9	52	52	NUM
fcis-3933	79	10	use	use	VERB
fcis-3933	79	11	the	the	DET
fcis-3933	79	12	standard	standard	ADJ
fcis-3933	79	13	cnn	cnn	PROPN
fcis-3933	79	14	network	network	NOUN
fcis-3933	79	15	to	to	PART
fcis-3933	79	16	extract	extract	VERB
fcis-3933	79	17	the	the	DET
fcis-3933	79	18	features	feature	NOUN
fcis-3933	79	19	of	of	ADP
fcis-3933	79	20	the	the	DET
fcis-3933	79	21	text	text	NOUN
fcis-3933	79	22	image	image	NOUN
fcis-3933	79	23	,	,	PUNCT
fcis-3933	79	24	then	then	ADV
fcis-3933	79	25	use	use	VERB
fcis-3933	79	26	blstm	blstm	NOUN
fcis-3933	79	27	to	to	PART
fcis-3933	79	28	fuse	fuse	VERB
fcis-3933	79	29	the	the	DET
fcis-3933	79	30	feature	feature	NOUN
fcis-3933	79	31	vectors	vector	NOUN
fcis-3933	79	32	to	to	PART
fcis-3933	79	33	extract	extract	VERB
fcis-3933	79	34	the	the	DET
fcis-3933	79	35	context	context	NOUN
fcis-3933	79	36	features	feature	NOUN
fcis-3933	79	37	of	of	ADP
fcis-3933	79	38	the	the	DET
fcis-3933	79	39	character	character	NOUN
fcis-3933	79	40	sequence	sequence	NOUN
fcis-3933	79	41	,	,	PUNCT
fcis-3933	79	42	then	then	ADV
fcis-3933	79	43	obtain	obtain	VERB
fcis-3933	79	44	the	the	DET
fcis-3933	79	45	probability	probability	NOUN
fcis-3933	79	46	distribution	distribution	NOUN
fcis-3933	79	47	of	of	ADP
fcis-3933	79	48	each	each	DET
fcis-3933	79	49	column	column	NOUN
fcis-3933	79	50	of	of	ADP
fcis-3933	79	51	features	feature	NOUN
fcis-3933	79	52	,	,	PUNCT
fcis-3933	79	53	and	and	CCONJ
fcis-3933	79	54	finally	finally	ADV
fcis-3933	79	55	predict	predict	VERB
fcis-3933	79	56	the	the	DET
fcis-3933	79	57	text	text	NOUN
fcis-3933	79	58	sequence	sequence	NOUN
fcis-3933	79	59	through	through	ADP
fcis-3933	79	60	the	the	DET
fcis-3933	79	61	transcription	transcription	NOUN
fcis-3933	79	62	layer	layer	NOUN
fcis-3933	79	63	(	(	PUNCT
fcis-3933	79	64	ctc	ctc	PROPN
fcis-3933	79	65	)	)	PUNCT
fcis-3933	79	66	.	.	PUNCT
fcis-3933	80	1	4)model	4)model	NUM
fcis-3933	80	2	training	train	VERB
fcis-3933	80	3	the	the	DET
fcis-3933	80	4	specific	specific	ADJ
fcis-3933	80	5	model	model	NOUN
fcis-3933	80	6	training	training	NOUN
fcis-3933	80	7	process	process	NOUN
fcis-3933	80	8	is	be	AUX
fcis-3933	80	9	as	as	SCONJ
fcis-3933	80	10	follows	follow	VERB
fcis-3933	80	11	:	:	PUNCT
fcis-3933	80	12	a	a	X
fcis-3933	80	13	)	)	PUNCT
fcis-3933	80	14	zoom	zoom	VERB
fcis-3933	80	15	the	the	DET
fcis-3933	80	16	input	input	NOUN
fcis-3933	80	17	image	image	NOUN
fcis-3933	80	18	to	to	ADP
fcis-3933	80	19	32	32	NUM
fcis-3933	80	20	*	*	PUNCT
fcis-3933	81	1	w	w	PROPN
fcis-3933	81	2	*	*	PUNCT
fcis-3933	81	3	3	3	NUM
fcis-3933	81	4	b	b	NOUN
fcis-3933	81	5	)	)	PUNCT
fcis-3933	81	6	use	use	VERB
fcis-3933	81	7	cnn	cnn	PROPN
fcis-3933	81	8	to	to	PART
fcis-3933	81	9	extract	extract	VERB
fcis-3933	81	10	the	the	DET
fcis-3933	81	11	image	image	NOUN
fcis-3933	81	12	convolution	convolution	NOUN
fcis-3933	81	13	feature	feature	NOUN
fcis-3933	81	14	,	,	PUNCT
fcis-3933	81	15	and	and	CCONJ
fcis-3933	81	16	the	the	DET
fcis-3933	81	17	size	size	NOUN
fcis-3933	81	18	is	be	AUX
fcis-3933	81	19	1	1	NUM
fcis-3933	81	20	*	*	SYM
fcis-3933	81	21	w/4	w/4	NOUN
fcis-3933	81	22	*	*	PUNCT
fcis-3933	81	23	512	512	NUM
fcis-3933	81	24	.	.	PUNCT
fcis-3933	82	1	c	c	X
fcis-3933	82	2	)	)	PUNCT
fcis-3933	82	3	extract	extract	VERB
fcis-3933	82	4	sequence	sequence	NOUN
fcis-3933	82	5	features	feature	NOUN
fcis-3933	82	6	from	from	ADP
fcis-3933	82	7	lstm	lstm	NOUN
fcis-3933	82	8	through	through	ADP
fcis-3933	82	9	the	the	DET
fcis-3933	82	10	above	above	ADJ
fcis-3933	82	11	input	input	NOUN
fcis-3933	82	12	to	to	PART
fcis-3933	82	13	obtain	obtain	VERB
fcis-3933	82	14	w/4	w/4	NOUN
fcis-3933	82	15	*	*	PUNCT
fcis-3933	83	1	n	n	DET
fcis-3933	83	2	posterior	posterior	ADJ
fcis-3933	83	3	probability	probability	NOUN
fcis-3933	83	4	matrix	matrix	NOUN
fcis-3933	83	5	d	d	NOUN
fcis-3933	83	6	)	)	PUNCT
fcis-3933	83	7	utilize	utilize	VERB
fcis-3933	83	8	ctc	ctc	NOUN
fcis-3933	83	9	loss	loss	NOUN
fcis-3933	83	10	to	to	PART
fcis-3933	83	11	realize	realize	VERB
fcis-3933	83	12	one	one	NUM
fcis-3933	83	13	-	-	PUNCT
fcis-3933	83	14	to	to	ADP
fcis-3933	83	15	-	-	PUNCT
fcis-3933	83	16	one	one	NUM
fcis-3933	83	17	correspondence	correspondence	NOUN
fcis-3933	83	18	between	between	ADP
fcis-3933	83	19	labels	label	NOUN
fcis-3933	83	20	and	and	CCONJ
fcis-3933	83	21	outputs	output	NOUN
fcis-3933	83	22	for	for	ADP
fcis-3933	83	23	training	training	NOUN
fcis-3933	83	24	.	.	PUNCT
fcis-3933	84	1	6	6	X
fcis-3933	84	2	.	.	X
fcis-3933	84	3	conclusion	conclusion	NOUN
fcis-3933	84	4	limited	limit	VERB
fcis-3933	84	5	by	by	ADP
fcis-3933	84	6	traditional	traditional	ADJ
fcis-3933	84	7	computer	computer	NOUN
fcis-3933	84	8	vision	vision	NOUN
fcis-3933	84	9	algorithms	algorithm	NOUN
fcis-3933	84	10	,	,	PUNCT
fcis-3933	84	11	traditional	traditional	ADJ
fcis-3933	84	12	ocr	ocr	NOUN
fcis-3933	84	13	only	only	ADV
fcis-3933	84	14	performs	perform	VERB
fcis-3933	84	15	well	well	ADV
fcis-3933	84	16	on	on	ADP
fcis-3933	84	17	regular	regular	ADJ
fcis-3933	84	18	printed	print	VERB
fcis-3933	84	19	documents	document	NOUN
fcis-3933	84	20	,	,	PUNCT
fcis-3933	84	21	such	such	ADJ
fcis-3933	84	22	as	as	ADP
fcis-3933	84	23	high	high	ADJ
fcis-3933	84	24	-	-	PUNCT
fcis-3933	84	25	quality	quality	NOUN
fcis-3933	84	26	scanned	scan	VERB
fcis-3933	84	27	documents	document	NOUN
fcis-3933	84	28	.	.	PUNCT
fcis-3933	85	1	because	because	SCONJ
fcis-3933	85	2	traditional	traditional	ADJ
fcis-3933	85	3	ocr	ocr	NOUN
fcis-3933	85	4	often	often	ADV
fcis-3933	85	5	relies	rely	VERB
fcis-3933	85	6	on	on	ADP
fcis-3933	85	7	complex	complex	ADJ
fcis-3933	85	8	process	process	NOUN
fcis-3933	85	9	optimization	optimization	NOUN
fcis-3933	85	10	and	and	CCONJ
fcis-3933	85	11	manual	manual	ADJ
fcis-3933	85	12	design	design	NOUN
fcis-3933	85	13	to	to	PART
fcis-3933	85	14	adapt	adapt	VERB
fcis-3933	85	15	to	to	ADP
fcis-3933	85	16	scenarios	scenario	NOUN
fcis-3933	85	17	,	,	PUNCT
fcis-3933	85	18	the	the	DET
fcis-3933	85	19	scenarios	scenario	NOUN
fcis-3933	85	20	have	have	VERB
fcis-3933	85	21	poor	poor	ADJ
fcis-3933	85	22	universality	universality	NOUN
fcis-3933	85	23	,	,	PUNCT
fcis-3933	85	24	and	and	CCONJ
fcis-3933	85	25	in	in	ADP
fcis-3933	85	26	different	different	ADJ
fcis-3933	85	27	business	business	NOUN
fcis-3933	85	28	scenarios	scenario	NOUN
fcis-3933	85	29	,	,	PUNCT
fcis-3933	85	30	a	a	DET
fcis-3933	85	31	lot	lot	NOUN
fcis-3933	85	32	of	of	ADP
fcis-3933	85	33	manual	manual	ADJ
fcis-3933	85	34	fine	fine	ADV
fcis-3933	85	35	-	-	PUNCT
fcis-3933	85	36	tuning	tuning	NOUN
fcis-3933	85	37	is	be	AUX
fcis-3933	85	38	often	often	ADV
fcis-3933	85	39	required	require	VERB
fcis-3933	85	40	to	to	PART
fcis-3933	85	41	adapt	adapt	VERB
fcis-3933	85	42	to	to	ADP
fcis-3933	85	43	differences	difference	NOUN
fcis-3933	85	44	;	;	PUNCT
fcis-3933	85	45	under	under	ADP
fcis-3933	85	46	complex	complex	ADJ
fcis-3933	85	47	scenes	scene	NOUN
fcis-3933	85	48	,	,	PUNCT
fcis-3933	85	49	the	the	DET
fcis-3933	85	50	performance	performance	NOUN
fcis-3933	85	51	and	and	CCONJ
fcis-3933	85	52	accuracy	accuracy	NOUN
fcis-3933	85	53	of	of	ADP
fcis-3933	85	54	character	character	NOUN
fcis-3933	85	55	recognition	recognition	NOUN
fcis-3933	85	56	are	be	AUX
fcis-3933	85	57	not	not	PART
fcis-3933	85	58	ideal	ideal	ADJ
fcis-3933	85	59	.	.	PUNCT
fcis-3933	86	1	text	text	NOUN
fcis-3933	86	2	recognition	recognition	NOUN
fcis-3933	86	3	for	for	ADP
fcis-3933	86	4	images	image	NOUN
fcis-3933	86	5	in	in	ADP
fcis-3933	86	6	natural	natural	ADJ
fcis-3933	86	7	scenes	scene	NOUN
fcis-3933	86	8	is	be	AUX
fcis-3933	86	9	much	much	ADV
fcis-3933	86	10	more	more	ADV
fcis-3933	86	11	difficult	difficult	ADJ
fcis-3933	86	12	than	than	ADP
fcis-3933	86	13	text	text	NOUN
fcis-3933	86	14	recognition	recognition	NOUN
fcis-3933	86	15	in	in	ADP
fcis-3933	86	16	conventional	conventional	ADJ
fcis-3933	86	17	scanned	scan	VERB
fcis-3933	86	18	document	document	NOUN
fcis-3933	86	19	images	image	NOUN
fcis-3933	86	20	,	,	PUNCT
fcis-3933	86	21	because	because	SCONJ
fcis-3933	86	22	the	the	DET
fcis-3933	86	23	form	form	NOUN
fcis-3933	86	24	of	of	ADP
fcis-3933	86	25	text	text	NOUN
fcis-3933	86	26	presentation	presentation	NOUN
fcis-3933	86	27	in	in	ADP
fcis-3933	86	28	natural	natural	ADJ
fcis-3933	86	29	scenes	scene	NOUN
fcis-3933	86	30	is	be	AUX
fcis-3933	86	31	very	very	ADV
fcis-3933	86	32	complex	complex	ADJ
fcis-3933	86	33	and	and	CCONJ
fcis-3933	86	34	irregular	irregular	ADJ
fcis-3933	86	35	.	.	PUNCT
fcis-3933	87	1	crnn	crnn	NOUN
fcis-3933	87	2	model	model	NOUN
fcis-3933	87	3	is	be	AUX
fcis-3933	87	4	trained	train	VERB
fcis-3933	87	5	to	to	PART
fcis-3933	87	6	solve	solve	VERB
fcis-3933	87	7	the	the	DET
fcis-3933	87	8	problem	problem	NOUN
fcis-3933	87	9	that	that	PRON
fcis-3933	87	10	some	some	DET
fcis-3933	87	11	chinese	chinese	PROPN
fcis-3933	87	12	can	can	AUX
fcis-3933	87	13	not	not	PART
fcis-3933	87	14	be	be	AUX
fcis-3933	87	15	recognized	recognize	VERB
fcis-3933	87	16	,	,	PUNCT
fcis-3933	87	17	reduce	reduce	VERB
fcis-3933	87	18	the	the	DET
fcis-3933	87	19	recognition	recognition	NOUN
fcis-3933	87	20	error	error	NOUN
fcis-3933	87	21	rate	rate	NOUN
fcis-3933	87	22	of	of	ADP
fcis-3933	87	23	some	some	DET
fcis-3933	87	24	characters	character	NOUN
fcis-3933	87	25	caused	cause	VERB
fcis-3933	87	26	by	by	ADP
fcis-3933	87	27	background	background	NOUN
fcis-3933	87	28	interference	interference	NOUN
fcis-3933	87	29	.	.	PUNCT
fcis-3933	88	1	references	reference	NOUN
fcis-3933	88	2	[	[	X
fcis-3933	88	3	1	1	NUM
fcis-3933	88	4	]	]	X
fcis-3933	88	5	zhang	zhang	PROPN
fcis-3933	88	6	tingting	tingting	PROPN
fcis-3933	88	7	.	.	PUNCT
fcis-3933	89	1	based	base	VERB
fcis-3933	89	2	on	on	ADP
fcis-3933	89	3	tesseract	tesseract	ADJ
fcis-3933	89	4	_	_	PUNCT
fcis-3933	89	5	research	research	NOUN
fcis-3933	89	6	on	on	ADP
fcis-3933	89	7	ocr	ocr	ADJ
fcis-3933	89	8	character	character	NOUN
fcis-3933	89	9	recognition	recognition	NOUN
fcis-3933	89	10	system	system	NOUN
fcis-3933	90	1	[	[	X
fcis-3933	90	2	d	d	X
fcis-3933	90	3	]	]	X
fcis-3933	90	4	.	.	PUNCT
fcis-3933	91	1	nanjing	nanjing	PROPN
fcis-3933	91	2	:	:	PUNCT
fcis-3933	91	3	nanjing	nanjing	PROPN
fcis-3933	91	4	university	university	PROPN
fcis-3933	91	5	of	of	ADP
fcis-3933	91	6	posts	post	NOUN
fcis-3933	91	7	and	and	CCONJ
fcis-3933	91	8	telecommunications	telecommunication	NOUN
fcis-3933	91	9	,	,	PUNCT
fcis-3933	91	10	2020	2020	NUM
fcis-3933	91	11	.	.	PUNCT
fcis-3933	92	1	[	[	X
fcis-3933	92	2	2	2	X
fcis-3933	92	3	]	]	PUNCT
fcis-3933	92	4	wang	wang	PROPN
fcis-3933	92	5	yiwen	yiwen	PROPN
fcis-3933	92	6	.	.	PUNCT
fcis-3933	93	1	research	research	NOUN
fcis-3933	93	2	on	on	ADP
fcis-3933	93	3	the	the	DET
fcis-3933	93	4	application	application	NOUN
fcis-3933	93	5	of	of	ADP
fcis-3933	93	6	deep	deep	ADJ
fcis-3933	93	7	convolution	convolution	NOUN
fcis-3933	93	8	neural	neural	ADJ
fcis-3933	93	9	network	network	NOUN
fcis-3933	93	10	in	in	ADP
fcis-3933	93	11	ocr	ocr	PROPN
fcis-3933	94	1	[	[	X
fcis-3933	94	2	d	d	X
fcis-3933	94	3	]	]	X
fcis-3933	94	4	.	.	PUNCT
fcis-3933	95	1	chengdu	chengdu	PROPN
fcis-3933	95	2	:	:	PUNCT
fcis-3933	95	3	university	university	NOUN
fcis-3933	95	4	of	of	ADP
fcis-3933	95	5	electronic	electronic	ADJ
fcis-3933	95	6	science	science	NOUN
fcis-3933	95	7	and	and	CCONJ
fcis-3933	95	8	technology	technology	NOUN
fcis-3933	95	9	of	of	ADP
fcis-3933	95	10	china	china	PROPN
fcis-3933	95	11	,	,	PUNCT
fcis-3933	95	12	2018	2018	NUM
fcis-3933	95	13	.	.	PUNCT
fcis-3933	96	1	[	[	X
fcis-3933	96	2	3	3	X
fcis-3933	96	3	]	]	X
fcis-3933	96	4	wang	wang	PROPN
fcis-3933	96	5	yang	yang	PROPN
fcis-3933	96	6	,	,	PUNCT
fcis-3933	96	7	li	li	PROPN
fcis-3933	96	8	zhendong	zhendong	PROPN
fcis-3933	96	9	,	,	PUNCT
fcis-3933	96	10	yang	yang	PROPN
fcis-3933	96	11	guanci	guanci	PROPN
fcis-3933	96	12	.	.	PUNCT
fcis-3933	97	1	research	research	NOUN
fcis-3933	97	2	on	on	ADP
fcis-3933	97	3	the	the	DET
fcis-3933	97	4	application	application	NOUN
fcis-3933	97	5	of	of	ADP
fcis-3933	97	6	ocr	ocr	ADJ
fcis-3933	97	7	character	character	NOUN
fcis-3933	97	8	recognition	recognition	NOUN
fcis-3933	97	9	based	base	VERB
fcis-3933	97	10	on	on	ADP
fcis-3933	97	11	deep	deep	ADJ
fcis-3933	97	12	learning	learning	NOUN
fcis-3933	97	13	in	in	ADP
fcis-3933	97	14	the	the	DET
fcis-3933	97	15	banking	banking	NOUN
fcis-3933	97	16	industry	industry	NOUN
fcis-3933	98	1	[	[	X
fcis-3933	98	2	j	j	X
fcis-3933	98	3	]	]	X
fcis-3933	98	4	.	.	PUNCT
fcis-3933	99	1	computer	computer	NOUN
fcis-3933	99	2	application	application	NOUN
fcis-3933	99	3	research	research	NOUN
fcis-3933	99	4	,	,	PUNCT
fcis-3933	99	5	2020	2020	NUM
fcis-3933	99	6	.	.	PUNCT
fcis-3933	100	1	[	[	X
fcis-3933	100	2	4	4	NUM
fcis-3933	100	3	]	]	X
fcis-3933	100	4	zhao	zhao	X
fcis-3933	100	5	shanshan	shanshan	PROPN
fcis-3933	100	6	.	.	PUNCT
fcis-3933	100	7	design	design	NOUN
fcis-3933	100	8	and	and	CCONJ
fcis-3933	100	9	implementation	implementation	NOUN
fcis-3933	100	10	of	of	ADP
fcis-3933	100	11	image	image	NOUN
fcis-3933	100	12	form	form	NOUN
fcis-3933	100	13	data	datum	NOUN
fcis-3933	100	14	recognition	recognition	NOUN
fcis-3933	100	15	system	system	NOUN
fcis-3933	100	16	based	base	VERB
fcis-3933	100	17	on	on	ADP
fcis-3933	100	18	ocr	ocr	PROPN
fcis-3933	100	19	technology[d	technology[d	PROPN
fcis-3933	100	20	]	]	PUNCT
fcis-3933	100	21	.	.	PUNCT
fcis-3933	101	1	nanjing	nanjing	PROPN
fcis-3933	101	2	:	:	PUNCT
fcis-3933	101	3	southeast	southeast	PROPN
fcis-3933	101	4	university	university	NOUN
fcis-3933	101	5	,	,	PUNCT
fcis-3933	101	6	2020	2020	NUM
fcis-3933	101	7	.	.	PUNCT
fcis-3933	102	1	[	[	X
fcis-3933	102	2	5	5	X
fcis-3933	102	3	]	]	PUNCT
fcis-3933	102	4	zeng	zeng	PROPN
fcis-3933	102	5	yue	yue	PROPN
fcis-3933	102	6	,	,	PUNCT
fcis-3933	102	7	ma	ma	PROPN
fcis-3933	102	8	mingdong	mingdong	PROPN
fcis-3933	102	9	.	.	PUNCT
fcis-3933	103	1	research	research	NOUN
fcis-3933	103	2	on	on	ADP
fcis-3933	103	3	character	character	NOUN
fcis-3933	103	4	recognition	recognition	NOUN
fcis-3933	103	5	based	base	VERB
fcis-3933	103	6	on	on	ADP
fcis-3933	103	7	tesseract_ocr	tesseract_ocr	NUM
fcis-3933	103	8	[	[	X
fcis-3933	103	9	j	j	X
fcis-3933	103	10	]	]	X
fcis-3933	103	11	.	.	PUNCT
fcis-3933	104	1	computer	computer	NOUN
fcis-3933	104	2	technology	technology	NOUN
fcis-3933	104	3	and	and	CCONJ
fcis-3933	104	4	development	development	NOUN
fcis-3933	104	5	,	,	PUNCT
fcis-3933	104	6	2021	2021	NUM
fcis-3933	104	7	.	.	PUNCT
fcis-3933	105	1	[	[	X
fcis-3933	105	2	6	6	NUM
fcis-3933	105	3	]	]	X
fcis-3933	105	4	xiao	xiao	PROPN
fcis-3933	105	5	jian	jian	PROPN
fcis-3933	105	6	.	.	PUNCT
fcis-3933	106	1	learning	learn	VERB
fcis-3933	106	2	based	base	VERB
fcis-3933	106	3	ocr	ocr	ADJ
fcis-3933	106	4	character	character	NOUN
fcis-3933	106	5	recognition	recognition	NOUN
fcis-3933	107	1	[	[	X
fcis-3933	107	2	j	j	X
fcis-3933	107	3	]	]	X
fcis-3933	107	4	.	.	PUNCT
fcis-3933	108	1	the	the	DET
fcis-3933	108	2	computer	computer	NOUN
fcis-3933	108	3	age	age	NOUN
fcis-3933	108	4	,	,	PUNCT
fcis-3933	108	5	2018	2018	NUM
fcis-3933	108	6	.	.	PUNCT
fcis-3933	109	1	[	[	X
fcis-3933	109	2	7	7	X
fcis-3933	109	3	]	]	X
fcis-3933	109	4	han	han	PROPN
fcis-3933	109	5	ping	ping	PROPN
fcis-3933	109	6	,	,	PUNCT
fcis-3933	109	7	liu	liu	PROPN
fcis-3933	109	8	zexu	zexu	PROPN
fcis-3933	109	9	.	.	PUNCT
fcis-3933	110	1	an	an	DET
fcis-3933	110	2	improved	improved	ADJ
fcis-3933	110	3	method	method	NOUN
fcis-3933	110	4	of	of	ADP
fcis-3933	110	5	x	x	ADJ
fcis-3933	110	6	-	-	NOUN
fcis-3933	110	7	ray	ray	NOUN
fcis-3933	110	8	baggage	baggage	NOUN
fcis-3933	110	9	image	image	NOUN
fcis-3933	110	10	enhancement	enhancement	NOUN
fcis-3933	110	11	based	base	VERB
fcis-3933	110	12	on	on	ADP
fcis-3933	110	13	gray	gray	ADJ
fcis-3933	110	14	level	level	NOUN
fcis-3933	110	15	grouping	grouping	NOUN
fcis-3933	110	16	[	[	X
fcis-3933	110	17	j	j	X
fcis-3933	110	18	]	]	X
fcis-3933	110	19	.	.	PUNCT
fcis-3933	111	1	journal	journal	PROPN
fcis-3933	111	2	of	of	ADP
fcis-3933	111	3	civil	civil	ADJ
fcis-3933	111	4	aviation	aviation	NOUN
fcis-3933	111	5	university	university	PROPN
fcis-3933	111	6	of	of	ADP
fcis-3933	111	7	china	china	PROPN
fcis-3933	111	8	,	,	PUNCT
fcis-3933	111	9	2011	2011	NUM
fcis-3933	111	10	,	,	PUNCT
fcis-3933	111	11	29	29	NUM
fcis-3933	111	12	(	(	PUNCT
fcis-3933	111	13	4	4	NUM
fcis-3933	111	14	):	):	PUNCT
fcis-3933	111	15	2326	2326	NUM
fcis-3933	111	16	.	.	PUNCT