Unconstrained Arabic Scene Text Analysis using Concurrent Invariant Points

Ahmed, SB; Naz, S; Razzak, I; Prasad, M

Unconstrained Arabic Scene Text Analysis using Concurrent Invariant Points

Ahmed, SB Naz, S Razzak, I 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. 1-6
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Ahmed, SB
dc.contributor.author	Naz, S
dc.contributor.author	Razzak, I
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253
dc.date	2020-07-19
dc.date.accessioned	2021-03-16T01:11:40Z
dc.date.available	2021-03-16T01:11:40Z
dc.date.issued	2020-07-01
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2020, 00, pp. 1-6
dc.identifier.isbn	9781728169262
dc.identifier.uri	http://hdl.handle.net/10453/147222
dc.description.abstract	© 2020 IEEE. Text in natural scene image portrays rich semantic information that plays an important role in content analysis. However, apart from Arabic text in documents, the text in natural scene images exhibit much higher diversity and variability, especially in uncontrolled circumstances. In this paper, a hybrid feature extraction approach is presented to detect extremal region of Arabic scene text. The binary image and image mask are considered as a variant of input image and look for concurrent extremal regions in both images. After determination of conjoined extremal points, the scale invariant technique is applied to consider those invariant points which are common in both images based on their coordinate positions. To evaluate the performance, a multidimensional long short term memory (LSTM) network is adapted and obtained 94.21% accuracy for word recognition on unconstrained Arabic scene text recognition (ASTR) dataset.
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/IJCNN48605.2020.9207283
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.rights	info:eu-repo/semantics/closedAccess
dc.title	Unconstrained Arabic Scene Text Analysis using Concurrent Invariant Points
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	2021-03-16T01:11:38Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-19
pubs.volume	00

Abstract:

© 2020 IEEE. Text in natural scene image portrays rich semantic information that plays an important role in content analysis. However, apart from Arabic text in documents, the text in natural scene images exhibit much higher diversity and variability, especially in uncontrolled circumstances. In this paper, a hybrid feature extraction approach is presented to detect extremal region of Arabic scene text. The binary image and image mask are considered as a variant of input image and look for concurrent extremal regions in both images. After determination of conjoined extremal points, the scale invariant technique is applied to consider those invariant points which are common in both images based on their coordinate positions. To evaluate the performance, a multidimensional long short term memory (LSTM) network is adapted and obtained 94.21% accuracy for word recognition on unconstrained Arabic scene text recognition (ASTR) dataset.

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http://hdl.handle.net/10453/147222