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Handwriting word recognition using windowed Bernoulli HMMs

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Handwriting word recognition using windowed Bernoulli HMMs

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dc.contributor.author Giménez Pastor, Adrián es_ES
dc.contributor.author Alkhoury, Ihab es_ES
dc.contributor.author Andrés Ferrer, Jesús es_ES
dc.contributor.author Juan Císcar, Alfonso es_ES
dc.date.accessioned 2014-05-08T13:42:35Z
dc.date.issued 2014-01
dc.identifier.issn 0167-8655
dc.identifier.uri http://hdl.handle.net/10251/37326
dc.description.abstract [EN] Hidden Markov Models (HMMs) are now widely used for off-line handwriting recognition in many lan- guages. As in speech recognition, they are usually built from shared, embedded HMMs at symbol level, where state-conditional probability density functions in each HMM are modeled with Gaussian mixtures. In contrast to speech recognition, however, it is unclear which kind of features should be used and, indeed, very different features sets are in use today. Among them, we have recently proposed to directly use columns of raw, binary image pixels, which are directly fed into embedded Bernoulli (mixture) HMMs, that is, embedded HMMs in which the emission probabilities are modeled with Bernoulli mix- tures. The idea is to by-pass feature extraction and to ensure that no discriminative information is filtered out during feature extraction, which in some sense is integrated into the recognition model. In this work, column bit vectors are extended by means of a sliding window of adequate width to better capture image context at each horizontal position of the word image. Using these windowed Bernoulli mixture HMMs, good results are reported on the well-known IAM and RIMES databases of Latin script, and in particular, state-of-the-art results are provided on the IfN/ENIT database of Arabic handwritten words. es_ES
dc.format.extent 8 es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
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 HMMs es_ES
dc.subject Latin es_ES
dc.subject Arabig es_ES
dc.subject Sliding window es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Handwriting word recognition using windowed Bernoulli HMMs es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.patrec.2012.09.002
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario Mixto Tecnológico de Informática - Institut Universitari Mixt Tecnològic d'Informàtica 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.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Giménez Pastor, A.; Alkhoury, I.; Andrés Ferrer, J.; Juan Císcar, A. (2014). Handwriting word recognition using windowed Bernoulli HMMs. Pattern Recognition Letters. 35:149-156. doi:10.1016/j.patrec.2012.09.002 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.patrec.2012.09.002 es_ES
dc.description.upvformatpinicio 149 es_ES
dc.description.upvformatpfin 156 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 35 es_ES
dc.relation.senia 243418


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