Discriminative Bernoulli HMMs for isolated handwritten word recognition

[EN] Bernoulli HMMs (BHMMs) have been successfully applied to handwritten text recognition (HTR) tasks such as continuous and isolated handwritten words. BHMMs belong to the generative model family and, hence, are usually trained by (joint) maximum likelihood estimation (MLE) by means of the Baum-We...

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Detalhes bibliográficos
Autores: Giménez Pastor, Adrián, Andrés Ferrer, Jesús, Juan, Alfons
Formato: artículo
Fecha de publicación:2014
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/50978
Acesso em linha:https://riunet.upv.es/handle/10251/50978
Access Level:acceso abierto
Palavra-chave:HTR
Bernoulli HMM
Log-linear HMM
MMI
RIMES
ESTADISTICA E INVESTIGACION OPERATIVA
LENGUAJES Y SISTEMAS INFORMATICOS
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spelling Discriminative Bernoulli HMMs for isolated handwritten word recognitionGiménez Pastor, AdriánAndrés Ferrer, JesúsJuan, AlfonsHTRBernoulli HMMLog-linear HMMMMIRIMESESTADISTICA E INVESTIGACION OPERATIVALENGUAJES Y SISTEMAS INFORMATICOS[EN] Bernoulli HMMs (BHMMs) have been successfully applied to handwritten text recognition (HTR) tasks such as continuous and isolated handwritten words. BHMMs belong to the generative model family and, hence, are usually trained by (joint) maximum likelihood estimation (MLE) by means of the Baum-Welch algorithm. Despite the good properties of the MLE criterion, there are better training criteria such as maximum mutual information (MM!). The MMI is the most widespread criterion to train discriminative models such as log-linear (or maximum entropy) models. Inspired by a BHMM classifier, in this work, a log-linear HMM (LLHMM) for binary data is proposed. The proposed model is proved to be equivalent to the BHMM classifier, and, in this way, a discriminative training framework for BHMM classifiers is defined. The behavior of the proposed discriminative training framework is deeply studied in a well known task of isolated word recognition, the RIMES database. (C) 2013 Elsevier B.V. All rights reserved.Work supported by the EC (FEDER/FSE) and the Spanish MEC/MICINN under the MIPRCV ‘‘Consolider Ingenio 2010’’ program (CSD2007-00018), iTrans2 (TIN2009-14511) and MITTRAL (TIN2009-14633-C03-01) projects. Also supported by the IST Programme of the European Community, under the PASCAL2 Network of Excellence, IST-2007-216886, and by the Spanish MITyC under the erudito.com (TSI-020110-2009-439).ElsevierDepartamento de Sistemas Informáticos y ComputaciónEscuela Técnica Superior de Ingeniería InformáticaMinisterio de Educación y CienciaMinisterio de Ciencia e InnovaciónMinisterio de Industria, Turismo y ComercioEuropean CommissionRepositorio Institucional de la Universitat Politècnica de València Riunet20142014-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/50978reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengEuropean Commission https://doi.org/10.13039/501100000780 FP7 216886 Pattern Analysis, Statistical Modelling and Computational Learning 2Ministerio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 TIN2009-14633-C03-01 Multimodal Interaction For Text Transcription With Adaptive LearningMinisterio de Educación y Ciencia https://doi.org/10.13039/501100008743 CSD2007-00018 Multimodal Intraction in Pattern Recognition and Computer VisionmMinisterio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 TIN2009-14511 Traduccion De Textos Y Transcripcion De Voz InteractivasMinisterio de Industria, Turismo y Comercio MITURCO TSI-020110-2009-0439 ERUDITO.COMopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/509782026-06-13T07:49:27Z
dc.title.none.fl_str_mv Discriminative Bernoulli HMMs for isolated handwritten word recognition
title Discriminative Bernoulli HMMs for isolated handwritten word recognition
spellingShingle Discriminative Bernoulli HMMs for isolated handwritten word recognition
Giménez Pastor, Adrián
HTR
Bernoulli HMM
Log-linear HMM
MMI
RIMES
ESTADISTICA E INVESTIGACION OPERATIVA
LENGUAJES Y SISTEMAS INFORMATICOS
title_short Discriminative Bernoulli HMMs for isolated handwritten word recognition
title_full Discriminative Bernoulli HMMs for isolated handwritten word recognition
title_fullStr Discriminative Bernoulli HMMs for isolated handwritten word recognition
title_full_unstemmed Discriminative Bernoulli HMMs for isolated handwritten word recognition
title_sort Discriminative Bernoulli HMMs for isolated handwritten word recognition
dc.creator.none.fl_str_mv Giménez Pastor, Adrián
Andrés Ferrer, Jesús
Juan, Alfons
author Giménez Pastor, Adrián
author_facet Giménez Pastor, Adrián
Andrés Ferrer, Jesús
Juan, Alfons
author_role author
author2 Andrés Ferrer, Jesús
Juan, Alfons
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Sistemas Informáticos y Computación
Escuela Técnica Superior de Ingeniería Informática
Ministerio de Educación y Ciencia
Ministerio de Ciencia e Innovación
Ministerio de Industria, Turismo y Comercio
European Commission
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv HTR
Bernoulli HMM
Log-linear HMM
MMI
RIMES
ESTADISTICA E INVESTIGACION OPERATIVA
LENGUAJES Y SISTEMAS INFORMATICOS
topic HTR
Bernoulli HMM
Log-linear HMM
MMI
RIMES
ESTADISTICA E INVESTIGACION OPERATIVA
LENGUAJES Y SISTEMAS INFORMATICOS
description [EN] Bernoulli HMMs (BHMMs) have been successfully applied to handwritten text recognition (HTR) tasks such as continuous and isolated handwritten words. BHMMs belong to the generative model family and, hence, are usually trained by (joint) maximum likelihood estimation (MLE) by means of the Baum-Welch algorithm. Despite the good properties of the MLE criterion, there are better training criteria such as maximum mutual information (MM!). The MMI is the most widespread criterion to train discriminative models such as log-linear (or maximum entropy) models. Inspired by a BHMM classifier, in this work, a log-linear HMM (LLHMM) for binary data is proposed. The proposed model is proved to be equivalent to the BHMM classifier, and, in this way, a discriminative training framework for BHMM classifiers is defined. The behavior of the proposed discriminative training framework is deeply studied in a well known task of isolated word recognition, the RIMES database. (C) 2013 Elsevier B.V. All rights reserved.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-01-01
dc.type.none.fl_str_mv journal 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://riunet.upv.es/handle/10251/50978
url https://riunet.upv.es/handle/10251/50978
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission https://doi.org/10.13039/501100000780 FP7 216886 Pattern Analysis, Statistical Modelling and Computational Learning 2
Ministerio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 TIN2009-14633-C03-01 Multimodal Interaction For Text Transcription With Adaptive Learning
Ministerio de Educación y Ciencia https://doi.org/10.13039/501100008743 CSD2007-00018 Multimodal Intraction in Pattern Recognition and Computer Visionm
Ministerio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 TIN2009-14511 Traduccion De Textos Y Transcripcion De Voz Interactivas
Ministerio de Industria, Turismo y Comercio MITURCO TSI-020110-2009-0439 ERUDITO.COM
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
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
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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