Text detection in born-digital images by mass estimation

Xu, J; Shivakumara, P; Lu, T; Tan, CL; Blumenstein, M

Text detection in born-digital images by mass estimation

Xu, J Shivakumara, P Lu, T Tan, CL Blumenstein, M

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015, 2016, pp. 690 - 694
Issue Date:: 2016-06-07

Closed Access

	Filename	Description	Size
	xu.pdf	Published version	467.61 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Xu, J	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.date.issued	2016-06-07	en_US
dc.identifier.citation	Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015, 2016, pp. 690 - 694	en_US
dc.identifier.isbn	9781479961009	en_US
dc.identifier.uri	http://hdl.handle.net/10453/102966
dc.description.abstract	© 2015 IEEE. There is a need for effective web-document understanding due to the explosive progress of internet and network technologies. In this paper, we propose a new method for text detection in born-digital images by introducing a mass estimation concept. We propose to explore super-pixel information of different color channels to identify text atoms in images. The proposed method uses similarity graphs and spectral clustering to identify candidate text regions. We propose a new idea of mapping Gabor responses of a candidate text region to a spatial circle to study the spatial coherency of pixels. We introduce a mass estimation concept to identify text candidates from the pixel distribution in a spatial circle. The linear linkage graphs help in grouping text candidates to obtain full text lines. The same Gabor responses are used as features to eliminate false positives with an SVM classifier. We evaluate the proposed method for the testing on standard datasets, such as ICDAR 2013 (challenge-1) and the Situ et al. dataset. Experimental results on both the datasets show that the proposed method outperforms the existing methods.	en_US
dc.relation.ispartof	Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015	en_US
dc.relation.isbasedon	10.1109/ACPR.2015.7486591	en_US
dc.title	Text detection in born-digital images by mass estimation	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	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

Abstract:

© 2015 IEEE. There is a need for effective web-document understanding due to the explosive progress of internet and network technologies. In this paper, we propose a new method for text detection in born-digital images by introducing a mass estimation concept. We propose to explore super-pixel information of different color channels to identify text atoms in images. The proposed method uses similarity graphs and spectral clustering to identify candidate text regions. We propose a new idea of mapping Gabor responses of a candidate text region to a spatial circle to study the spatial coherency of pixels. We introduce a mass estimation concept to identify text candidates from the pixel distribution in a spatial circle. The linear linkage graphs help in grouping text candidates to obtain full text lines. The same Gabor responses are used as features to eliminate false positives with an SVM classifier. We evaluate the proposed method for the testing on standard datasets, such as ICDAR 2013 (challenge-1) and the Situ et al. dataset. Experimental results on both the datasets show that the proposed method outperforms the existing methods.

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

http://hdl.handle.net/10453/102966