Hindi off-line signature verification

Pal, S; Blumenstein, M; Pal, U

Hindi off-line signature verification

Pal, S Blumenstein, M

Pal, U

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR, 2012, pp. 373 - 378
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Pal, S	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.contributor.author	Pal, U	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR, 2012, pp. 373 - 378	en_US
dc.identifier.isbn	9780769547749	en_US
dc.identifier.issn	1550-5235	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121228
dc.description.abstract	Handwritten Signatures are one of the widely used biometrics for document authentication as well as human authorization. The purpose of this paper is to present an offline signature verification system involving Hindi signatures. Signature verification is a process by which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite of substantial research in the field of signature verification involving Western signatures, very little attention has been dedicated to non-Western signatures such as Chinese, Japanese, Arabic, Persian etc. In this paper, the performance of an off-line signature verification system involving Hindi signatures, whose style is distinct from Western scripts, has been investigated. The gradient and Zernike moment features were employed and Support Vector Machines (SVMs) were considered for verification. To the best of the authors' knowledge, Hindi signatures have never been used for the task of signature verification and this is the first report of using Hindi signatures in this area. The Hindi signature database employed for experimentation consisted of 840 (35×24) genuine signatures and 1050 (35×30) forgeries. An encouraging accuracy of 7.42% FRR and 4.28% FAR were obtained following experimentation when the gradient features were employed. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR	en_US
dc.relation.isbasedon	10.1109/ICFHR.2012.212	en_US
dc.title	Hindi off-line signature verification	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:

Handwritten Signatures are one of the widely used biometrics for document authentication as well as human authorization. The purpose of this paper is to present an offline signature verification system involving Hindi signatures. Signature verification is a process by which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite of substantial research in the field of signature verification involving Western signatures, very little attention has been dedicated to non-Western signatures such as Chinese, Japanese, Arabic, Persian etc. In this paper, the performance of an off-line signature verification system involving Hindi signatures, whose style is distinct from Western scripts, has been investigated. The gradient and Zernike moment features were employed and Support Vector Machines (SVMs) were considered for verification. To the best of the authors' knowledge, Hindi signatures have never been used for the task of signature verification and this is the first report of using Hindi signatures in this area. The Hindi signature database employed for experimentation consisted of 840 (35×24) genuine signatures and 1050 (35×30) forgeries. An encouraging accuracy of 7.42% FRR and 4.28% FAR were obtained following experimentation when the gradient features were employed. © 2012 IEEE.

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http://hdl.handle.net/10453/121228