A blind deconvolution model for scene text detection and recognition in video

Khare, V; Shivakumara, P; Raveendran, P; Blumenstein, M

A blind deconvolution model for scene text detection and recognition in video

Khare, V Shivakumara, P Raveendran, P Blumenstein, M

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Publication Type:: Journal Article
Citation:: Pattern Recognition, 2016, 54 pp. 128 - 148
Issue Date:: 2016-06-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Khare, V	en_US
dc.contributor.author	Shivakumara, P	en_US
dc.contributor.author	Raveendran, P	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.issued	2016-06-01	en_US
dc.identifier.citation	Pattern Recognition, 2016, 54 pp. 128 - 148	en_US
dc.identifier.issn	0031-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121788
dc.description.abstract	© 2016 Elsevier Ltd. All rights reserved. Text detection and recognition in poor quality video is a challenging problem due to unpredictable blur and distortion effects caused by camera and text movements. This affects the overall performance of the text detection and recognition methods. This paper presents a combined quality metric for estimating the degree of blur in the video/image. Then the proposed method introduces a blind deconvolution model that enhances the edge intensity by suppressing blurred pixels. The proposed deblurring model is compared with other state-of-the-art models to demonstrate its superiority. In addition, to validate the usefulness and the effectiveness of the proposed model, we conducted text detection and recognition experiments on blurred images classified by the proposed model from standard video databases, namely, ICDAR 2013, ICDAR 2015, YVT and then standard natural scene image databases, namely, ICDAR 2013, SVT, MSER. Text detection and recognition results on both blurred and deblurred video/images illustrate that the proposed model improves the performance significantly.	en_US
dc.relation.ispartof	Pattern Recognition	en_US
dc.relation.isbasedon	10.1016/j.patcog.2016.01.008	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A blind deconvolution model for scene text detection and recognition in video	en_US
dc.type	Journal Article
utslib.citation.volume	54	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0806 Information Systems	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.publication-status	Published	en_US
pubs.volume	54	en_US

Abstract:

© 2016 Elsevier Ltd. All rights reserved. Text detection and recognition in poor quality video is a challenging problem due to unpredictable blur and distortion effects caused by camera and text movements. This affects the overall performance of the text detection and recognition methods. This paper presents a combined quality metric for estimating the degree of blur in the video/image. Then the proposed method introduces a blind deconvolution model that enhances the edge intensity by suppressing blurred pixels. The proposed deblurring model is compared with other state-of-the-art models to demonstrate its superiority. In addition, to validate the usefulness and the effectiveness of the proposed model, we conducted text detection and recognition experiments on blurred images classified by the proposed model from standard video databases, namely, ICDAR 2013, ICDAR 2015, YVT and then standard natural scene image databases, namely, ICDAR 2013, SVT, MSER. Text detection and recognition results on both blurred and deblurred video/images illustrate that the proposed model improves the performance significantly.

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

http://hdl.handle.net/10453/121788