A study on idiosyncratic handwriting with impact on writer identification

Adak, C; Chaudhuri, BB; Blumenstein, M

A study on idiosyncratic handwriting with impact on writer identification

Adak, C

Chaudhuri, BB 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. 193 - 198
Issue Date:: 2018-12-05

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Field	Value	Language
dc.contributor.author	Adak, C https://orcid.org/0000-0002-9085-2770	en_US
dc.contributor.author	Chaudhuri, BB	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.available	2020-12-05T18:06:21Z
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. 193 - 198	en_US
dc.identifier.isbn	9781538658758	en_US
dc.identifier.issn	2167-6445	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132808
dc.description.abstract	© 2018 IEEE. In this paper, we study handwriting idiosyncrasy in terms of its structural eccentricity. In this study, our approach is to find idiosyncratic handwritten text components and model the idiosyncrasy analysis task as a machine learning problem supervised by human cognition. We employ the Inception network for this purpose. The experiments are performed on two publicly available databases and an in-house database of Bengali offline handwritten samples. On these samples, subjective opinion scores of handwriting idiosyncrasy are collected from handwriting experts. We have analyzed the handwriting idiosyncrasy on this corpus which comprises the perceptive ground-truth opinion. We also investigate the effect of idiosyncratic text on writer identification by using the SqueezeNet. The performance of our system is promising.	en_US
dc.relation.ispartof	Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR	en_US
dc.relation.isbasedon	10.1109/ICFHR-2018.2018.00042	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A study on idiosyncratic handwriting with impact on writer identification	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/Faculty of Engineering and Information Technology/School of Computer Science
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	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-August	en_US

Abstract:

© 2018 IEEE. In this paper, we study handwriting idiosyncrasy in terms of its structural eccentricity. In this study, our approach is to find idiosyncratic handwritten text components and model the idiosyncrasy analysis task as a machine learning problem supervised by human cognition. We employ the Inception network for this purpose. The experiments are performed on two publicly available databases and an in-house database of Bengali offline handwritten samples. On these samples, subjective opinion scores of handwriting idiosyncrasy are collected from handwriting experts. We have analyzed the handwriting idiosyncrasy on this corpus which comprises the perceptive ground-truth opinion. We also investigate the effect of idiosyncratic text on writer identification by using the SqueezeNet. The performance of our system is promising.

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http://hdl.handle.net/10453/132808