A two-stage approach for English and Hindi off-line signature verification

Pal, S; Pal, U; Blumenstein, M

A two-stage approach for English and Hindi off-line signature verification

Pal, S Pal, U Blumenstein, M

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8158 LNCS pp. 140 - 148
Issue Date:: 2013-11-11

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Field	Value	Language
dc.contributor.author	Pal, S	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	2013-11-11	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8158 LNCS pp. 140 - 148	en_US
dc.identifier.isbn	9783642411892	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120842
dc.description.abstract	The purpose of this paper is to present an empirical contribution towards the understanding of multi-script off-line signature identification and verification using a novel method involving off-line Hindi (Devnagari) and English signatures. The main aim of this approach is to demonstrate the significant advantage of the use of signature script identification in a multi-script signature verification environment. In the 1st stage of the proposed signature verification technique a script identification technique is employed to know whether a signature is written in Hindi or English. In the second stage, a verification approach was explored separately for English signatures and Hindi signatures based on the script identification result. Different features like gradient feature, water reservoir feature, loop feature, aspect ratio etc. were employed, and Support Vector Machines (SVMs) were considered in our scheme. To get the comparative idea, multi-script signature verification results on the joint Hindi and English dataset without using any script identification technique is also computed. From the experiment results it is noted that we are able to reduce average error rate 4.81% more when script identification method is employed. © 2013 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-41190-8_16	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A two-stage approach for English and Hindi off-line signature verification	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8158 LNCS	en_US
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
pubs.volume	8158 LNCS	en_US

Abstract:

The purpose of this paper is to present an empirical contribution towards the understanding of multi-script off-line signature identification and verification using a novel method involving off-line Hindi (Devnagari) and English signatures. The main aim of this approach is to demonstrate the significant advantage of the use of signature script identification in a multi-script signature verification environment. In the 1st stage of the proposed signature verification technique a script identification technique is employed to know whether a signature is written in Hindi or English. In the second stage, a verification approach was explored separately for English signatures and Hindi signatures based on the script identification result. Different features like gradient feature, water reservoir feature, loop feature, aspect ratio etc. were employed, and Support Vector Machines (SVMs) were considered in our scheme. To get the comparative idea, multi-script signature verification results on the joint Hindi and English dataset without using any script identification technique is also computed. From the experiment results it is noted that we are able to reduce average error rate 4.81% more when script identification method is employed. © 2013 Springer-Verlag.

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http://hdl.handle.net/10453/120842