A PGM-based System for Arabic HandwrittenWord Recognition

This paper describes a system for off-line recognition of handwritten Arabic words. It uses simple andeasily extractable features to construct feature vectors for words in the vocabulary. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are...

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Detalhes bibliográficos
Autores: Kacem Echi, Afef|||0000-0001-9219-5228, Khémiri, Akram, Belaïd, Abdel
Formato: artículo
Fecha de publicación:2014
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:125595
Acesso em linha:https://ddd.uab.cat/record/125595
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.575
Access Level:acceso abierto
Palavra-chave:Feature and image descriptors
Image modelling
Statistical pattern recognition
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spelling A PGM-based System for Arabic HandwrittenWord RecognitionKacem Echi, Afef|||0000-0001-9219-5228Khémiri, AkramBelaïd, AbdelFeature and image descriptorsImage modellingStatistical pattern recognitionThis paper describes a system for off-line recognition of handwritten Arabic words. It uses simple andeasily extractable features to construct feature vectors for words in the vocabulary. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders and diacritic points. The system is evolved based on horizontal and vertical Hidden Markov Models and Dynamic Bayesian Network. Our strategy consists of looking for various architectures and selecting those which provide the best recognition performance. Experiments on handwritten Arabic words from IFN/ENIT database and ancient manuscripts strongly support the feasibility of the proposed system. The recognition rates achieve 91.89% (IFN/ENIT) and 94.61% (ancient manuscripts). 22014-01-0120142014-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/125595https://dx.doi.org/urn:doi:10.5565/rev/elcvia.575reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.https://creativecommons.org/licenses/by-nc-nd/3.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:1255952026-06-06T12:50:31Z
dc.title.none.fl_str_mv A PGM-based System for Arabic HandwrittenWord Recognition
title A PGM-based System for Arabic HandwrittenWord Recognition
spellingShingle A PGM-based System for Arabic HandwrittenWord Recognition
Kacem Echi, Afef|||0000-0001-9219-5228
Feature and image descriptors
Image modelling
Statistical pattern recognition
title_short A PGM-based System for Arabic HandwrittenWord Recognition
title_full A PGM-based System for Arabic HandwrittenWord Recognition
title_fullStr A PGM-based System for Arabic HandwrittenWord Recognition
title_full_unstemmed A PGM-based System for Arabic HandwrittenWord Recognition
title_sort A PGM-based System for Arabic HandwrittenWord Recognition
dc.creator.none.fl_str_mv Kacem Echi, Afef|||0000-0001-9219-5228
Khémiri, Akram
Belaïd, Abdel
author Kacem Echi, Afef|||0000-0001-9219-5228
author_facet Kacem Echi, Afef|||0000-0001-9219-5228
Khémiri, Akram
Belaïd, Abdel
author_role author
author2 Khémiri, Akram
Belaïd, Abdel
author2_role author
author
dc.subject.none.fl_str_mv Feature and image descriptors
Image modelling
Statistical pattern recognition
topic Feature and image descriptors
Image modelling
Statistical pattern recognition
description This paper describes a system for off-line recognition of handwritten Arabic words. It uses simple andeasily extractable features to construct feature vectors for words in the vocabulary. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders and diacritic points. The system is evolved based on horizontal and vertical Hidden Markov Models and Dynamic Bayesian Network. Our strategy consists of looking for various architectures and selecting those which provide the best recognition performance. Experiments on handwritten Arabic words from IFN/ENIT database and ancient manuscripts strongly support the feasibility of the proposed system. The recognition rates achieve 91.89% (IFN/ENIT) and 94.61% (ancient manuscripts).
publishDate 2014
dc.date.none.fl_str_mv 2
2014-01-01
2014
2014-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/125595
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.575
url https://ddd.uab.cat/record/125595
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.575
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/3.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/3.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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