An application of deep learning in character recognition: An overview

Saeed, S; Naz, S; Razzak, MI

An application of deep learning in character recognition: An overview

Saeed, S Naz, S Razzak, MI

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Handbook of Deep Learning Applications, 2019, 136, pp. 53-81
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Saeed, S
dc.contributor.author	Naz, S
dc.contributor.author	Razzak, MI
dc.date.accessioned	2020-05-26T23:00:12Z
dc.date.available	2020-05-26T23:00:12Z
dc.date.issued	2019-01-01
dc.identifier.citation	Handbook of Deep Learning Applications, 2019, 136, pp. 53-81
dc.identifier.isbn	978-3-030-11478-7
dc.identifier.uri	http://hdl.handle.net/10453/140963
dc.description.abstract	© 2019, Springer Nature Switzerland AG. For automated document analysis, OCR (Optical character recognition) is a basic building block. The robust automated document analysis system can have impact over a wider sphere of life. Many of the researchers have been working hard to build OCR systems in various languages with significant degree of accuracy, character recognition rate and minimum error rate. Deep learning is the start of art technique with efficient and accurate result as compared to other techniques. Every language, moreover every script have its own challenges e.g. scripts where characters are well separated are less challenging as compared to cursive scripts where characters are attached with one another. In this chapter, we would take a detailed account of the state of art deep learning techniques for Arabic like script, Latin script and symbolic script.
dc.format.extent	17
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Handbook of Deep Learning Applications
dc.relation.isbasedon	10.1007/2F978-3-030-11479-4_3
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	An application of deep learning in character recognition: An overview
dc.type	Chapter
utslib.citation.volume	136
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-26T23:00:07Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.volume	136
utslib.start-page	53
dc.location	Switzerland

Abstract:

© 2019, Springer Nature Switzerland AG. For automated document analysis, OCR (Optical character recognition) is a basic building block. The robust automated document analysis system can have impact over a wider sphere of life. Many of the researchers have been working hard to build OCR systems in various languages with significant degree of accuracy, character recognition rate and minimum error rate. Deep learning is the start of art technique with efficient and accurate result as compared to other techniques. Every language, moreover every script have its own challenges e.g. scripts where characters are well separated are less challenging as compared to cursive scripts where characters are attached with one another. In this chapter, we would take a detailed account of the state of art deep learning techniques for Arabic like script, Latin script and symbolic script.

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http://hdl.handle.net/10453/140963