Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition

Over the last few years, several researchers have worked on handwritten character recognition and have proposed various techniques to improve the performance of Indic and non-Indic scripts recognition. Here, a Deep Convolutional Neural Network has been proposed that learns deep features for offline...

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Detalles Bibliográficos
Autores: Mahto, Manoj Kumar|||0000-0002-8258-055X, Bhatia, Karamjit, Sharma, Rajendra Kumar
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:258035
Acceso en línea:https://ddd.uab.cat/record/258035
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1282
Access Level:acceso abierto
Palabra clave:Character and text recognition
Handwritten recognition
Document analysis
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spelling Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral RecognitionMahto, Manoj Kumar|||0000-0002-8258-055XBhatia, KaramjitSharma, Rajendra KumarCharacter and text recognitionHandwritten recognitionDocument analysisOver the last few years, several researchers have worked on handwritten character recognition and have proposed various techniques to improve the performance of Indic and non-Indic scripts recognition. Here, a Deep Convolutional Neural Network has been proposed that learns deep features for offline Gurmukhi handwritten character and numeral recognition (HCNR). The proposed network works efficiently for training as well as testing and exhibits a good recognition performance. Two primary datasets comprising of offline handwritten Gurmukhi characters and Gurmukhi numerals have been employed in the present work. The testing accuracies achieved using the proposed network is 98.5% for characters and 98.6% for numerals. 22021-01-0120212021-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/258035https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1282reponame: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/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2580352026-06-06T12:50:31Z
dc.title.none.fl_str_mv Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition
title Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition
spellingShingle Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition
Mahto, Manoj Kumar|||0000-0002-8258-055X
Character and text recognition
Handwritten recognition
Document analysis
title_short Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition
title_full Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition
title_fullStr Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition
title_full_unstemmed Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition
title_sort Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition
dc.creator.none.fl_str_mv Mahto, Manoj Kumar|||0000-0002-8258-055X
Bhatia, Karamjit
Sharma, Rajendra Kumar
author Mahto, Manoj Kumar|||0000-0002-8258-055X
author_facet Mahto, Manoj Kumar|||0000-0002-8258-055X
Bhatia, Karamjit
Sharma, Rajendra Kumar
author_role author
author2 Bhatia, Karamjit
Sharma, Rajendra Kumar
author2_role author
author
dc.subject.none.fl_str_mv Character and text recognition
Handwritten recognition
Document analysis
topic Character and text recognition
Handwritten recognition
Document analysis
description Over the last few years, several researchers have worked on handwritten character recognition and have proposed various techniques to improve the performance of Indic and non-Indic scripts recognition. Here, a Deep Convolutional Neural Network has been proposed that learns deep features for offline Gurmukhi handwritten character and numeral recognition (HCNR). The proposed network works efficiently for training as well as testing and exhibits a good recognition performance. Two primary datasets comprising of offline handwritten Gurmukhi characters and Gurmukhi numerals have been employed in the present work. The testing accuracies achieved using the proposed network is 98.5% for characters and 98.6% for numerals.
publishDate 2021
dc.date.none.fl_str_mv 2
2021-01-01
2021
2021-01-01
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/258035
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1282
url https://ddd.uab.cat/record/258035
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1282
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/4.0/
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rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
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
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