A new method for character segmentation from multi-oriented video words

Sharma, N; Shivakumara, P; Pal, U; Blumenstein, M; Tan, CL

A new method for character segmentation from multi-oriented video words

Sharma, N

Shivakumara, P Pal, U Blumenstein, M

Tan, CL

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 2013, pp. 413 - 417
Issue Date:: 2013-12-11

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Field	Value	Language
dc.contributor.author	Sharma, N https://orcid.org/0000-0003-0841-1245	en_US
dc.contributor.author	Shivakumara, P	en_US
dc.contributor.author	Pal, U	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.contributor.author	Tan, CL	en_US
dc.date.issued	2013-12-11	en_US
dc.identifier.citation	Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 2013, pp. 413 - 417	en_US
dc.identifier.issn	1520-5363	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120991
dc.description.abstract	This paper presents a two-stage method for multi-oriented video character segmentation. Words segmented from video text lines are considered for character segmentation in the present work. Words can contain isolated or non-touching characters, as well as touching characters. Therefore, the character segmentation problem can be viewed as a two stage problem. In the first stage, text cluster is identified and isolated (non-touching) characters are segmented. The orientation of each word is computed and the segmentation paths are found in the direction perpendicular to the orientation. Candidate segmentation points computed using the top distance profile are used to find the segmentation path between the characters considering the background cluster. In the second stage, the segmentation results are verified and a check is performed to ascertain whether the word component contains touching characters or not. The average width of the components is used to find the touching character components. For segmentation of the touching characters, segmentation points are then found using average stroke width information, along with the top and bottom distance profiles. The proposed method was tested on a large dataset and was evaluated in terms of precision, recall and f-measure. A comparative study with existing methods reveals the superiority of the proposed method. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the International Conference on Document Analysis and Recognition, ICDAR	en_US
dc.relation.isbasedon	10.1109/ICDAR.2013.90	en_US
dc.title	A new method for character segmentation from multi-oriented video words	en_US
dc.type	Conference Proceeding
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/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	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents a two-stage method for multi-oriented video character segmentation. Words segmented from video text lines are considered for character segmentation in the present work. Words can contain isolated or non-touching characters, as well as touching characters. Therefore, the character segmentation problem can be viewed as a two stage problem. In the first stage, text cluster is identified and isolated (non-touching) characters are segmented. The orientation of each word is computed and the segmentation paths are found in the direction perpendicular to the orientation. Candidate segmentation points computed using the top distance profile are used to find the segmentation path between the characters considering the background cluster. In the second stage, the segmentation results are verified and a check is performed to ascertain whether the word component contains touching characters or not. The average width of the components is used to find the touching character components. For segmentation of the touching characters, segmentation points are then found using average stroke width information, along with the top and bottom distance profiles. The proposed method was tested on a large dataset and was evaluated in terms of precision, recall and f-measure. A comparative study with existing methods reveals the superiority of the proposed method. © 2013 IEEE.

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http://hdl.handle.net/10453/120991