Multi-script off-line signature identification

Pal, S; Alireza, A; Pal, U; Blumenstein, M

Multi-script off-line signature identification

Pal, S Alireza, A Pal, U Blumenstein, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2012 12th International Conference on Hybrid Intelligent Systems, HIS 2012, 2012, pp. 236 - 240
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Pal, S	en_US
dc.contributor.author	Alireza, A	en_US
dc.contributor.author	Pal, U	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings of the 2012 12th International Conference on Hybrid Intelligent Systems, HIS 2012, 2012, pp. 236 - 240	en_US
dc.identifier.isbn	9781467351157	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121221
dc.description.abstract	In this paper, we present an empirical contribution towards the understanding of multi-script signature identification. In the proposed signature identification system, the signatures of Bengali (Bangla), Hindi (Devanagari) and English are considered for the identification process. This system will identify whether a claimed signature belongs to the group of Bengali, Hindi or English signatures. Zernike Moment and histogram of gradient are employed as two different feature extraction techniques. In the proposed system, Support Vector Machines (SVMs) are considered as classifiers for signature identification. A database of 2100 Bangla signatures, 2100 Hindi signatures and 2100 English signatures are used for experimentation. Two different results based on two different feature sets are calculated and analysed. The highest accuracy of 92.14% is obtained based on the gradient features using 4200 (1400 Bangla +1400 Hindi + 1400 English) samples for training and 2100 (700 Bangla +700 Hindi +700 English) samples for testing. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2012 12th International Conference on Hybrid Intelligent Systems, HIS 2012	en_US
dc.relation.isbasedon	10.1109/HIS.2012.6421340	en_US
dc.title	Multi-script off-line signature identification	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, we present an empirical contribution towards the understanding of multi-script signature identification. In the proposed signature identification system, the signatures of Bengali (Bangla), Hindi (Devanagari) and English are considered for the identification process. This system will identify whether a claimed signature belongs to the group of Bengali, Hindi or English signatures. Zernike Moment and histogram of gradient are employed as two different feature extraction techniques. In the proposed system, Support Vector Machines (SVMs) are considered as classifiers for signature identification. A database of 2100 Bangla signatures, 2100 Hindi signatures and 2100 English signatures are used for experimentation. Two different results based on two different feature sets are calculated and analysed. The highest accuracy of 92.14% is obtained based on the gradient features using 4200 (1400 Bangla +1400 Hindi + 1400 English) samples for training and 2100 (700 Bangla +700 Hindi +700 English) samples for testing. © 2012 IEEE.

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http://hdl.handle.net/10453/121221