Leveraging surrounding context for scene text detection

Li, Y; Shen, C; Jia, W; Van Den Hengel, A

Leveraging surrounding context for scene text detection

Li, Y Shen, C Jia, W

Van Den Hengel, A

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Publication Type:: Conference Proceeding
Citation:: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 2264 - 2268
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Shen, C	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Van Den Hengel, A	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 2264 - 2268	en_US
dc.identifier.isbn	9781479923410	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32680
dc.description.abstract	Finding text in natural images has been a challenging task in vision. At the core of state-of-the-art scene text detection algorithms are a set of text-specific features within extracted regions. In this paper, we attempt to solve this problem from a different prospective. We show that characters and non-character interferences are separable by leveraging the surrounding context. Surrounding context, in our work, is composed of two components which are computed in an information-theoretic fashion. Minimization of an energy cost function yields a binary label for each region, which indicates the category it belongs to. The proposed algorithm is fast, discriminative and tolerant to character variations and involves minimal parameter tuning. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICIP.2013.6738467	en_US
dc.title	Leveraging surrounding context for scene text detection	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne, Australia
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 - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Finding text in natural images has been a challenging task in vision. At the core of state-of-the-art scene text detection algorithms are a set of text-specific features within extracted regions. In this paper, we attempt to solve this problem from a different prospective. We show that characters and non-character interferences are separable by leveraging the surrounding context. Surrounding context, in our work, is composed of two components which are computed in an information-theoretic fashion. Minimization of an energy cost function yields a binary label for each region, which indicates the category it belongs to. The proposed algorithm is fast, discriminative and tolerant to character variations and involves minimal parameter tuning. © 2013 IEEE.

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http://hdl.handle.net/10453/32680