Text detection in born-digital images using IT-LBP

Zeng, C; Jia, W; He, X; Zhang, L

Text detection in born-digital images using IT-LBP

Zeng, C Jia, W

He, X

Zhang, L

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Publication Type:: Journal Article
Citation:: Journal of Algorithms and Computational Technology, 2014, 8 (1), pp. 127 - 142
Issue Date:: 2014-03-01

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Field	Value	Language
dc.contributor.author	Zeng, C	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Zhang, L	en_US
dc.date.issued	2014-03-01	en_US
dc.identifier.citation	Journal of Algorithms and Computational Technology, 2014, 8 (1), pp. 127 - 142	en_US
dc.identifier.issn	1748-3018	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34100
dc.description.abstract	Fine text detection plays a crucial role in a text detection algorithm as it is capable of removing the false alarms while keeping the detected text lines in coarse text detection. Good performance of a machine learning-based fine text detection heavily depends on the powerful feature to depict the characteristics of text. In this paper, a novel texture-based descriptor, named IT-LBP, is proposed by considering horizontal, vertical, diagonal and anti-diagonal directions of character strokes to better describe the texture of text. The new feature demonstrates its superiority by comparing with other texture-based features. The new feature is used to train an SVM classifier to further filter out non-text candidates. The ICDAR 2011 born-digital image dataset is used to evaluate and demonstrate the performance of the proposed method. Following the same performance evaluation criteria, the proposed method outperforms the winner algorithm of the ICDAR 2011 Robust Reading Competition Challenge 1.	en_US
dc.relation.ispartof	Journal of Algorithms and Computational Technology	en_US
dc.relation.isbasedon	10.1260/1748-3018.8.1.127	en_US
dc.title	Text detection in born-digital images using IT-LBP	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	8	en_US
utslib.for	0802 Computation Theory and Mathematics	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

Fine text detection plays a crucial role in a text detection algorithm as it is capable of removing the false alarms while keeping the detected text lines in coarse text detection. Good performance of a machine learning-based fine text detection heavily depends on the powerful feature to depict the characteristics of text. In this paper, a novel texture-based descriptor, named IT-LBP, is proposed by considering horizontal, vertical, diagonal and anti-diagonal directions of character strokes to better describe the texture of text. The new feature demonstrates its superiority by comparing with other texture-based features. The new feature is used to train an SVM classifier to further filter out non-text candidates. The ICDAR 2011 born-digital image dataset is used to evaluate and demonstrate the performance of the proposed method. Following the same performance evaluation criteria, the proposed method outperforms the winner algorithm of the ICDAR 2011 Robust Reading Competition Challenge 1.

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http://hdl.handle.net/10453/34100