Hindi and English off-line signature identification and verification

Pal, S; Pal, U; Blumenstein, M

Hindi and English off-line signature identification and verification

Pal, S Pal, U Blumenstein, M

Permalink

Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2013, 174 AISC pp. 905 - 910
Issue Date:: 2013-01-01

Closed Access

	Filename	Description	Size
	10.1007%2F978-81-322-0740-5_109.pdf	Published version	185.05 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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	2013-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2013, 174 AISC pp. 905 - 910	en_US
dc.identifier.isbn	9788132207399	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121047
dc.description.abstract	Biometric systems play a significant role in the field of information security as they are extremely required for user authentication. Signature identification and verification have a great importance for authentication intention. The purpose of this paper is to present an empirical contribution towards the understanding of multi-script (Hindi and English) signature verification. This system will identify whether a claimed signature belongs to the group of English signatures or Hindi signatures from a combined Hindi and English signature datasets and then it will verify signatures using these two resultant signature datasets (Hindi script signature and English script signatures) separately. The modified gradient feature and SVM classifier were employed for identification and verification purposes. To the best of authors' knowledge, the multi-script signature identification and verification has never been used for the task of signature verification and this is the first report of using Hindi and English signatures in this area. Two different results for identification and verification are calculated and analysed. The accuracy of 98.05% is obtained for the identification of signature script using 2160 (1080 Hindi + 1080 English) samples for training and 1080 (540 Hindi + 540 English) samples for testing. The resultant data sets obtained in script identification of signatures were used for verification purpose. The FRR, FAR for Hindi and English was obtained 8.0%, 4.0% and 12.0%, 10.0% respectively. © 2013 Springer.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-81-322-0740-5_109	en_US
dc.title	Hindi and English off-line signature identification and verification	en_US
dc.type	Conference Proceeding
utslib.citation.volume	174 AISC	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	174 AISC	en_US

Abstract:

Biometric systems play a significant role in the field of information security as they are extremely required for user authentication. Signature identification and verification have a great importance for authentication intention. The purpose of this paper is to present an empirical contribution towards the understanding of multi-script (Hindi and English) signature verification. This system will identify whether a claimed signature belongs to the group of English signatures or Hindi signatures from a combined Hindi and English signature datasets and then it will verify signatures using these two resultant signature datasets (Hindi script signature and English script signatures) separately. The modified gradient feature and SVM classifier were employed for identification and verification purposes. To the best of authors' knowledge, the multi-script signature identification and verification has never been used for the task of signature verification and this is the first report of using Hindi and English signatures in this area. Two different results for identification and verification are calculated and analysed. The accuracy of 98.05% is obtained for the identification of signature script using 2160 (1080 Hindi + 1080 English) samples for training and 1080 (540 Hindi + 540 English) samples for testing. The resultant data sets obtained in script identification of signatures were used for verification purpose. The FRR, FAR for Hindi and English was obtained 8.0%, 4.0% and 12.0%, 10.0% respectively. © 2013 Springer.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121047