An automatic student verification system utilising off-line Thai name components

Suwanwiwat, H; Das, A; Ferrer, MA; Pal, U; Blumenstein, M

An automatic student verification system utilising off-line Thai name components

Suwanwiwat, H Das, A Ferrer, MA Pal, U Blumenstein, M

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Publication Type:: Conference Proceeding
Citation:: DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications, 2017, 2017-December pp. 1 - 6
Issue Date:: 2017-12-19

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Field	Value	Language
dc.contributor.author	Suwanwiwat, H	en_US
dc.contributor.author	Das, A	en_US
dc.contributor.author	Ferrer, MA	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	2017-12-19	en_US
dc.identifier.citation	DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications, 2017, 2017-December pp. 1 - 6	en_US
dc.identifier.isbn	9781538628393	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126132
dc.description.abstract	© 2017 IEEE. This research proposed an automatic student identification and verification system utilising off-line Thai name components. The Thai name components consist of first and last names. Dense texture-based feature descriptors were able to yield encouraging results when applied to different handwritten text recognition scenarios. As a result, the authors employed such features in investigating their performance on Thai name component verification system. In this research, Dense-Local Binary Pattern, Dense-Local Directional Pattern, and Local Binary Pattern combined with Local Directional Pattern were employed. A base-line shape/feature i.e. Hidden Markov Model (HMM) was also utilised in this study. As there is no dataset on Thai name verification in the literature, a dataset is proposed for a Thai name verification system. The name component samples were collected from high school students. It consists of 8,400 name components (first and last names) from 100 students. Each student provided 60 genuine name components, and each of the name components was forged by 12 other students. An encouraging result was found employing the above-mentioned features on the proposed dataset.	en_US
dc.relation.ispartof	DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications	en_US
dc.relation.isbasedon	10.1109/DICTA.2017.8227406	en_US
dc.title	An automatic student verification system utilising off-line Thai name components	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-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	2017-December	en_US

Abstract:

© 2017 IEEE. This research proposed an automatic student identification and verification system utilising off-line Thai name components. The Thai name components consist of first and last names. Dense texture-based feature descriptors were able to yield encouraging results when applied to different handwritten text recognition scenarios. As a result, the authors employed such features in investigating their performance on Thai name component verification system. In this research, Dense-Local Binary Pattern, Dense-Local Directional Pattern, and Local Binary Pattern combined with Local Directional Pattern were employed. A base-line shape/feature i.e. Hidden Markov Model (HMM) was also utilised in this study. As there is no dataset on Thai name verification in the literature, a dataset is proposed for a Thai name verification system. The name component samples were collected from high school students. It consists of 8,400 name components (first and last names) from 100 students. Each student provided 60 genuine name components, and each of the name components was forged by 12 other students. An encouraging result was found employing the above-mentioned features on the proposed dataset.

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http://hdl.handle.net/10453/126132