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Handwriting recognition by using deep learning to extract meaningful features

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Handwriting recognition by using deep learning to extract meaningful features

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dc.contributor.author Pastor Pellicer, Joan es_ES
dc.contributor.author Castro-Bleda, Maria Jose es_ES
dc.contributor.author España Boquera, Salvador es_ES
dc.contributor.author Zamora-Martinez, Francisco Julián es_ES
dc.date.accessioned 2020-03-06T13:13:54Z
dc.date.available 2020-03-06T13:13:54Z
dc.date.issued 2019 es_ES
dc.identifier.issn 0921-7126 es_ES
dc.identifier.uri http://hdl.handle.net/10251/138465
dc.description.abstract [EN] Recent improvements in deep learning techniques show that deep models can extract more meaningful data directly from raw signals than conventional parametrization techniques, making it possible to avoid specific feature extraction in the area of pattern recognition, especially for Computer Vision or Speech tasks. In this work, we directly use raw text line images by feeding them to Convolutional Neural Networks and deep Multilayer Perceptrons for feature extraction in a Handwriting Recognition system. The proposed recognition system, based on Hidden Markov Models that are hybridized with Neural Networks, has been tested with the IAM Database, achieving a considerable improvement. es_ES
dc.description.sponsorship Work partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R. es_ES
dc.language Inglés es_ES
dc.publisher IOS Press es_ES
dc.relation.ispartof AI Communications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Handwriting recognition es_ES
dc.subject Deep learning es_ES
dc.subject Convolutional neural networks es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Handwriting recognition by using deep learning to extract meaningful features es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3233/AIC-170562 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85854-C4-2-R/ES/AMIC-UPV: ANALISIS AFECTIVO DE INFORMACION MULTIMEDIA CON COMUNICACION INCLUSIVA Y NATURAL/ es_ES
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.description.bibliographicCitation Pastor Pellicer, J.; Castro-Bleda, MJ.; España Boquera, S.; Zamora-Martinez, FJ. (2019). Handwriting recognition by using deep learning to extract meaningful features. AI Communications. 32(2):101-112. https://doi.org/10.3233/AIC-170562 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3233/AIC-170562 es_ES
dc.description.upvformatpinicio 101 es_ES
dc.description.upvformatpfin 112 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 32 es_ES
dc.description.issue 2 es_ES
dc.relation.pasarela S\388281 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
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