Legibility and Aesthetic Analysis of Handwriting

Adak, C; Chaudhuri, BB; Blumenstein, M

Legibility and Aesthetic Analysis of Handwriting

Adak, C

Chaudhuri, BB Blumenstein, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 2017, 1 pp. 175 - 182
Issue Date:: 2017-07-02

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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	2020-05-25T19:17:27Z
dc.date.issued	2017-07-02	en_US
dc.identifier.citation	Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 2017, 1 pp. 175 - 182	en_US
dc.identifier.isbn	9781538635865	en_US
dc.identifier.issn	1520-5363	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126492
dc.description.abstract	© 2017 IEEE. This paper deals with computer-based cognitive analysis towards legibility and aesthetics of a handwritten document. The legible text creates a human perception that the writing can be read effortlessly because of its orthographic clarity. The aesthetic property relates to the beautiful appearance of a handwritten document. In this study, we deal with these properties on offline Bengali handwriting. We formulate both legibility and aesthetic analysis tasks as machine learning problems supervised by the human cognitive system. We employ automatically derived feature-based recurrent neural networks to investigate writing legibility. For aesthetics evaluation, we employ hand-crafted feature-based support vector machines (SVMs). We have collected contemporary Bengali handwritings, on which the subjective legibility and aesthetic scores are provided by human readers. On this corpus containing legibility and aesthetic ground-Truth information, we executed our experiments. The experimental results obtained on various handwritings are encouraging.	en_US
dc.relation.ispartof	Proceedings of the International Conference on Document Analysis and Recognition, ICDAR	en_US
dc.relation.isbasedon	10.1109/ICDAR.2017.37	en_US
dc.title	Legibility and Aesthetic Analysis of Handwriting	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1	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	1	en_US

Abstract:

© 2017 IEEE. This paper deals with computer-based cognitive analysis towards legibility and aesthetics of a handwritten document. The legible text creates a human perception that the writing can be read effortlessly because of its orthographic clarity. The aesthetic property relates to the beautiful appearance of a handwritten document. In this study, we deal with these properties on offline Bengali handwriting. We formulate both legibility and aesthetic analysis tasks as machine learning problems supervised by the human cognitive system. We employ automatically derived feature-based recurrent neural networks to investigate writing legibility. For aesthetics evaluation, we employ hand-crafted feature-based support vector machines (SVMs). We have collected contemporary Bengali handwritings, on which the subjective legibility and aesthetic scores are provided by human readers. On this corpus containing legibility and aesthetic ground-Truth information, we executed our experiments. The experimental results obtained on various handwritings are encouraging.

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

http://hdl.handle.net/10453/126492