Contour Restoration of Text Components for Recognition in Video/Scene Images

Wu, Y; Shivakumara, P; Lu, T; Tan, CL; Blumenstein, M; Kumar, GH

Contour Restoration of Text Components for Recognition in Video/Scene Images

Wu, Y Shivakumara, P Lu, T Tan, CL Blumenstein, M

Kumar, GH

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2016, 25 (12), pp. 5622 - 5634
Issue Date:: 2016-12-01

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Field	Value	Language
dc.contributor.author	Wu, Y	en_US
dc.contributor.author	Shivakumara, P	en_US
dc.contributor.author	Lu, T	en_US
dc.contributor.author	Tan, CL	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.contributor.author	Kumar, GH	en_US
dc.date.issued	2016-12-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2016, 25 (12), pp. 5622 - 5634	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121811
dc.description.abstract	© 1992-2012 IEEE. Text recognition in video/natural scene images has gained significant attention in the field of image processing in many computer vision applications, which is much more challenging than recognition in plain background images. In this paper, we aim to restore complete character contours in video/scene images from gray values, in contrast to the conventional techniques that consider edge images/binary information as inputs for text detection and recognition. We explore and utilize the strengths of zero crossing points given by the Laplacian to identify stroke candidate pixels (SPC). For each SPC pair, we propose new symmetry features based on gradient magnitude and Fourier phase angles to identify probable stroke candidate pairs (PSCP). The same symmetry properties are proposed at the PSCP level to choose seed stroke candidate pairs (SSCP). Finally, an iterative algorithm is proposed for SSCP to restore complete character contours. Experimental results on benchmark databases, namely, the ICDAR family of video and natural scenes, Street View Data, and MSRA data sets, show that the proposed technique outperforms the existing techniques in terms of both quality measures and recognition rate. We also show that character contour restoration is effective for text detection in video and natural scene images.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2016.2607426	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Contour Restoration of Text Components for Recognition in Video/Scene Images	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	25	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	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/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.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

© 1992-2012 IEEE. Text recognition in video/natural scene images has gained significant attention in the field of image processing in many computer vision applications, which is much more challenging than recognition in plain background images. In this paper, we aim to restore complete character contours in video/scene images from gray values, in contrast to the conventional techniques that consider edge images/binary information as inputs for text detection and recognition. We explore and utilize the strengths of zero crossing points given by the Laplacian to identify stroke candidate pixels (SPC). For each SPC pair, we propose new symmetry features based on gradient magnitude and Fourier phase angles to identify probable stroke candidate pairs (PSCP). The same symmetry properties are proposed at the PSCP level to choose seed stroke candidate pairs (SSCP). Finally, an iterative algorithm is proposed for SSCP to restore complete character contours. Experimental results on benchmark databases, namely, the ICDAR family of video and natural scenes, Street View Data, and MSRA data sets, show that the proposed technique outperforms the existing techniques in terms of both quality measures and recognition rate. We also show that character contour restoration is effective for text detection in video and natural scene images.

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http://hdl.handle.net/10453/121811