Off-Line Handwritten Bilingual Name Recognition for Student Identification in an Automated Assessment System

Suwanwiwat, H; Nguyen, V; Blumenstein, M; Pal, U

Off-Line Handwritten Bilingual Name Recognition for Student Identification in an Automated Assessment System

Suwanwiwat, H Nguyen, V Blumenstein, M

Pal, U

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Publication Type:: Conference Proceeding
Citation:: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, 2014, 2014-December pp. 271 - 276
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Suwanwiwat, H	en_US
dc.contributor.author	Nguyen, V	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	2014-01-01	en_US
dc.identifier.citation	Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, 2014, 2014-December pp. 271 - 276	en_US
dc.identifier.isbn	9781479943340	en_US
dc.identifier.issn	2167-6445	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121090
dc.description.abstract	© 2014 IEEE. The Student name Identification System (SIS) proposed here was investigated for English and Thai languages combined. The proposed system recognises each name by using an approach for whole word recognition. In the proposed system, the Gaussian Grid Feature (GGF), and Modified Direction Feature (MDF), together with a proposed hybrid feature extraction technique called Water Reservoir, Loop and Gaussian Grid Feature (WRLGGF) were investigated on full word contour images of each name sample. Artificial neural networks and support vector machines were used as classifiers. An encouraging recognition accuracy of 99.25% was achieved employing the proposed technique compared to 98.59% for GGF, and 96.63% using MDF.	en_US
dc.relation.ispartof	Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR	en_US
dc.relation.isbasedon	10.1109/ICFHR.2014.53	en_US
dc.title	Off-Line Handwritten Bilingual Name Recognition for Student Identification in an Automated Assessment System	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2014-December	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	2014-December	en_US

Abstract:

© 2014 IEEE. The Student name Identification System (SIS) proposed here was investigated for English and Thai languages combined. The proposed system recognises each name by using an approach for whole word recognition. In the proposed system, the Gaussian Grid Feature (GGF), and Modified Direction Feature (MDF), together with a proposed hybrid feature extraction technique called Water Reservoir, Loop and Gaussian Grid Feature (WRLGGF) were investigated on full word contour images of each name sample. Artificial neural networks and support vector machines were used as classifiers. An encouraging recognition accuracy of 99.25% was achieved employing the proposed technique compared to 98.59% for GGF, and 96.63% using MDF.

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http://hdl.handle.net/10453/121090