End-to-End Analysis for Text Detection and Recognition in Natural Scene Images

Alnefaie, A; Gupta, D; Bhuyan, MH; Razzak, I; Gupta, P; Prasad, M

End-to-End Analysis for Text Detection and Recognition in Natural Scene Images

Alnefaie, A Gupta, D Bhuyan, MH Razzak, I Gupta, P Prasad, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2020, 00, pp. 1-8
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Alnefaie, A
dc.contributor.author	Gupta, D
dc.contributor.author	Bhuyan, MH
dc.contributor.author	Razzak, I
dc.contributor.author	Gupta, P
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253
dc.date	2020-07-19
dc.date.accessioned	2021-03-16T01:14:20Z
dc.date.available	2021-03-16T01:14:20Z
dc.date.issued	2020-07-01
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2020, 00, pp. 1-8
dc.identifier.isbn	9781728169262
dc.identifier.uri	http://hdl.handle.net/10453/147223
dc.description.abstract	© 2020 IEEE. Right from the very beginning, the text has vital importance in human life. As compared to the vision-based applications, preference is always given to the precise and productive information embodied in the text. Considering the importance of text, recognition, and detection of text is also equally important in human life. This paper presents a deep analysis of recent development on scene text and compare their performance and bring into light the real modern applications. Future potential directions of scene text detection and recognition are also discussed.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks
dc.relation.ispartof	2020 International Joint Conference on Neural Networks (IJCNN)
dc.relation.isbasedon	10.1109/IJCNN48605.2020.9207001
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	End-to-End Analysis for Text Detection and Recognition in Natural Scene Images
dc.type	Conference Proceeding
utslib.citation.volume	00
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 - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-16T01:14:18Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-19
pubs.volume	00

Abstract:

© 2020 IEEE. Right from the very beginning, the text has vital importance in human life. As compared to the vision-based applications, preference is always given to the precise and productive information embodied in the text. Considering the importance of text, recognition, and detection of text is also equally important in human life. This paper presents a deep analysis of recent development on scene text and compare their performance and bring into light the real modern applications. Future potential directions of scene text detection and recognition are also discussed.

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http://hdl.handle.net/10453/147223