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Discriminative Bernoulli HMMs for isolated handwritten word recognition

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Discriminative Bernoulli HMMs for isolated handwritten word recognition

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dc.contributor.author Giménez Pastor, Adrián es_ES
dc.contributor.author Andrés Ferrer, Jesús es_ES
dc.contributor.author Juan, Alfons es_ES
dc.date.accessioned 2015-05-29T11:51:30Z
dc.date.available 2015-05-29T11:51:30Z
dc.date.issued 2014-01-01
dc.identifier.issn 0167-8655
dc.identifier.uri http://hdl.handle.net/10251/50978
dc.description.abstract [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. es_ES
dc.description.sponsorship 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).
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation MEC-MICINN/CSD2007-00018 es_ES
dc.relation MEC-MICINN/TIN2009-14511 es_ES
dc.relation MITYC/TSI-020110-2009-439 es_ES
dc.relation MEC-MICINN/TIN2009-14633-C03-01
dc.relation info:eu-repo/grantAgreement/EC/FP7/216886/EU
dc.relation.ispartof Pattern Recognition Letters es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject HTR es_ES
dc.subject Bernoulli HMM es_ES
dc.subject Log-linear HMM es_ES
dc.subject MMI es_ES
dc.subject RIMES es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Discriminative Bernoulli HMMs for isolated handwritten word recognition es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso
dc.identifier.doi 10.1016/j.patrec.2013.05.016
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Giménez Pastor, A.; Andrés Ferrer, J.; Juan, A. (2014). Discriminative Bernoulli HMMs for isolated handwritten word recognition. Pattern Recognition Letters. 35:157-168. doi:10.1016/j.patrec.2013.05.016 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.conferencename 12th International Conference on Frontiers in Handwriting Recognition (ICFHR)
dc.relation.conferencedate 2010
dc.relation.conferenceplace Kolkata, India
dc.relation.publisherversion http://dx.doi.org/10.1016/10.1016/j.patrec.2013.05.016 es_ES
dc.description.upvformatpinicio 157 es_ES
dc.description.upvformatpfin 168 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 35 es_ES
dc.relation.senia 248332
dc.contributor.funder Ministerio de Educación y Ciencia
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.contributor.funder Ministerio de Industria, Turismo y Comercio
dc.contributor.funder European Commission


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