Text detection in born-digital images using multiple layer images

Zeng, C; Jia, W; He, X

Text detection in born-digital images using multiple layer images

Zeng, C Jia, W

He, X

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Publication Type:: Conference Proceeding
Citation:: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2013, pp. 1947 - 1951
Issue Date:: 2013-10-18

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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.date.issued	2013-10-18	en_US
dc.identifier.citation	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2013, pp. 1947 - 1951	en_US
dc.identifier.isbn	9781479903566	en_US
dc.identifier.issn	1520-6149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31941
dc.description.abstract	In this paper, a new framework for detecting text from webpage and email images is presented. The original image is split into multiple layer images based on the maximum gradient difference (MGD) values to detect text with both strong and weak contrasts. Connected component processing and text detection are performed in each layer image. A novel texture descriptor named T-LBP, is proposed to further filter out non-text candidates with a trained SVM classifier. 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. © 2013 IEEE.	en_US
dc.relation.ispartof	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICASSP.2013.6637993	en_US
dc.title	Text detection in born-digital images using multiple layer images	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
dc.location.activity	Vancouver Canada
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.publication-status	Published	en_US

Abstract:

In this paper, a new framework for detecting text from webpage and email images is presented. The original image is split into multiple layer images based on the maximum gradient difference (MGD) values to detect text with both strong and weak contrasts. Connected component processing and text detection are performed in each layer image. A novel texture descriptor named T-LBP, is proposed to further filter out non-text candidates with a trained SVM classifier. 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. © 2013 IEEE.

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http://hdl.handle.net/10453/31941