ICFHR 2018 Competition on Thai student signatures and name components recognition and verification (TSNCRV2018)

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

ICFHR 2018 Competition on Thai student signatures and name components recognition and verification (TSNCRV2018)

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

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Publication Type:: Conference Proceeding
Citation:: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, 2018, 2018-August pp. 500 - 505
Issue Date:: 2018-12-05

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Field	Value	Language
dc.contributor.author	Suwanwiwat, H	en_US
dc.contributor.author	Das, A	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	2018-12-05	en_US
dc.identifier.citation	Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, 2018, 2018-August pp. 500 - 505	en_US
dc.identifier.isbn	9781538658758	en_US
dc.identifier.issn	2167-6445	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132679
dc.description.abstract	© 2018 IEEE. This paper summarises the results of the competition on the 1st Thai Student Signature and Name Components Recognition and Verification (TSNCRV 2018). It was organised in the context of the 16th International Conference on Frontiers in Handwriting Recognition (ICFHR 2018). The aim of this competition was to record the development and gain attention on Thai student signatures and name component recognition and verification. Two different types of datasets were used for the competition: The first dataset contains Thai student signatures and the second dataset contains Thai student name components. Signatures and name components from 100 volunteers each were included in the competition datasets. For Thai signature dataset, there are 30 genuine signatures, 12 skilled and 12 simple forgeries for each writer. For Thai name components, there are 30 genuine and 12 skilfully forged name components for each writer. For both the datasets the individuals were asked to write their name/signature in the given space on a white piece of paper for number of time (with a pause between each 10 samples). The skilled forgers were asked practice emitting the original signature for certain number of times till they fill skilled to forge. Five teams from distinguish labs submitted their systems. This paper analysed the results produced by these algorithms/systems using a performance measure and defined a way forward for this subject of research. Both the datasets along with some of the accompanying ground truth/baseline mask will be made freely available for research purposes via the TC10/TC11.	en_US
dc.relation.ispartof	Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR	en_US
dc.relation.isbasedon	10.1109/ICFHR-2018.2018.00093	en_US
dc.title	ICFHR 2018 Competition on Thai student signatures and name components recognition and verification (TSNCRV2018)	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-August	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	2018-August	en_US

Abstract:

© 2018 IEEE. This paper summarises the results of the competition on the 1st Thai Student Signature and Name Components Recognition and Verification (TSNCRV 2018). It was organised in the context of the 16th International Conference on Frontiers in Handwriting Recognition (ICFHR 2018). The aim of this competition was to record the development and gain attention on Thai student signatures and name component recognition and verification. Two different types of datasets were used for the competition: The first dataset contains Thai student signatures and the second dataset contains Thai student name components. Signatures and name components from 100 volunteers each were included in the competition datasets. For Thai signature dataset, there are 30 genuine signatures, 12 skilled and 12 simple forgeries for each writer. For Thai name components, there are 30 genuine and 12 skilfully forged name components for each writer. For both the datasets the individuals were asked to write their name/signature in the given space on a white piece of paper for number of time (with a pause between each 10 samples). The skilled forgers were asked practice emitting the original signature for certain number of times till they fill skilled to forge. Five teams from distinguish labs submitted their systems. This paper analysed the results produced by these algorithms/systems using a performance measure and defined a way forward for this subject of research. Both the datasets along with some of the accompanying ground truth/baseline mask will be made freely available for research purposes via the TC10/TC11.

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http://hdl.handle.net/10453/132679