Microsoft Word - tpel_1.doc


Mathematical Problems of Computer Science 36, 115--120, 2012. 

 
115 

 

 
 

Off-Line Signature Verification and Recognition 
 

Vahe Khachaturyan 
 

Institute for Informatics and Automation Problems of NAS of RA 
e-mail: vahe@7smarts.com 

 
Abstract 

 
In this paper we propose a technique that can be used for signature 

recognition. This technique is a contour based technique. Here we propose a simple 
and effective approach that can be easily implemented in a programming language. 

The paper deals with the recognition of the signature, as human operator 
generally makes the work of signature recognition. Hence the algorithm simulates 
human behavior, to achieve perfection and skill through AI. The logic that decides the 
extent of validity of the signature must implement Artificial Intelligence Pattern 
recognition is the science that concerns the description or classification of 
measurements, usually based on underlying model. Since most pattern recognition 
tasks are first done by humans and automated later, the most fruitful source of features 
has been to ask the people who classify the objects how they tell them a part . 
Signatures are a behavioral biometric that change over a period of time and are 
influenced by physical and emotional conditions of a subject. This technique gives 
acceptable results in a simple and fast way. 

Key words: Pattern Recognition, Signature Recognition, Image Morphology 

 
References 

[1] M. Sonka, V. Hlavac and R. Boyle, “Image processing analysis, and machine vision”, 
Thomson Learning, Singapore, pp. 563-64, 573, 583, 593, 2002. 
[2] S. Loncaric, A. Dhawan,” A morphological signature transform for Shape Description.” 
Pattern Recognition, 6: pp. 1029-1037. 1993. 
[3] Evett and R. N. Totty, ”Study of the variation in the dimensions of genuine signatures”, 
Journal of the Forensic Science Society, vol. 25, pp. 207-215, 1985 
[4] B. Fang, C. H. Leung, Y. Y. Tang, K. W. Tse, P. C. K. Kwok, Y. K. Wong,”Off-Line 
signature verification by the tracking of feature and stroke positions”, Pattern Recognition, vol. 
36, pp. 91-101, 2003. 
[5] Y. Mizukami, H. Miike, M. Yoshimura, and I. Yoshimura, ”An Off-Line signature 
verification system using an extracted displacement function”, In Proceedings of ICDAR, pp. 
757-760, 1999. 
[6] W. F. Nemcek and W. C. Lin, ”Experimental investigation of automatic signature 
verification” IEEE Transactions on Systems, Man and Cybernetics, vol. 4 , pp. 121-126, 1974. 



Off-Line Signature Verification and Recognition 
 

116 

[7] T. Joachims, “Text categorization with support vector machines: Learning with many 
relevant features”. Proceedings of the Tenth European Conference on Machine Learning. Berlin: 
Springer, pp. 137 -142, 1998. 
 

 
 

<<úýɳÛÝ>> ëïáñ³·ñáõÃÛ³Ý ×³Ý³ãáõÙ 
 

ì. ʳã³ïáõñÛ³Ý 
 

²Ù÷á÷áõÙ 
 

 ²ß˳ï³ÝùáõÙ ³é³ç³ñÏíáõÙ ¿ <<úýɳÛÝ>> ëïáñ³·ñáõÃÛ³Ý ×³Ý³ãÙ³Ý »Õ³Ý³Ï՝ ÑÇÙÝí³Í 
»½ñ³·Í³ÛÇÝ Ý»ñÏÙ³Ý íñ³, ϳËí³Í ï³ñµ»ñ å³ñ³Ù»ïñ»ñÇó՝ ÷Çùë»ÉÝ»ñÇ ù³Ý³Ï, ß»ÕÙ³Ý 
³ÝÏÛáõÝ, ɳÛÝáõÃÛáõÝ, µ³ñÓñáõÃÛáõÝ: Ø»Ýù û·ï³·áñÍáõÙ»Ýù EX-Oring ëïáñ³·ñáõÃÛ³Ý 
ÇëÏáõÃÛáõÝÝ ëïáõ·»Éáõ ѳٳñ: ´»ñíáõÙ ¿ ëïáñ³·ñáõÃÛ³Ý ×³Ý³ãÙ³Ý ûñÇݳÏ: 
 
 
 

Опознание  “Офлайн” подписи  
 

В. Хачатурян 
 

Аннотация 
 

В работе предлагается метод опазнания ,,Офлайн” подписи основанный на 
контурное окрашивание в зависимости от многих параметров:  количество пикселеи, угол 
наклона, широта, высота. Мы  используем EX-Oring для проверки подлинности подписи. 
Приводится пример опазнания подписи.