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Querying out-of-vocabulary words in lexicon-based keyword spotting

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Querying out-of-vocabulary words in lexicon-based keyword spotting

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dc.contributor.author Puigcerver, Joan es_ES
dc.contributor.author Toselli, Alejandro Héctor es_ES
dc.contributor.author Vidal, Enrique es_ES
dc.date.accessioned 2017-06-09T10:47:46Z
dc.date.available 2017-06-09T10:47:46Z
dc.date.issued 2016-02
dc.identifier.issn 0941-0643
dc.identifier.uri http://hdl.handle.net/10251/82643
dc.description The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2197-8 es_ES
dc.description.abstract [EN] Lexicon-based handwritten text keyword spotting (KWS) has proven to be a faster and more accurate alternative to lexicon-free methods. Nevertheless, since lexicon-based KWS relies on a predefined vocabulary, fixed in the training phase, it does not support queries involving out-of-vocabulary (OOV) keywords. In this paper, we outline previous work aimed at solving this problem and present a new approach based on smoothing the (null) scores of OOV keywords by means of the information provided by ``similar'' in-vocabulary words. Good results achieved using this approach are compared with previously published alternatives on different data sets. es_ES
dc.description.sponsorship This work was partially supported by the Spanish MEC under FPU Grant FPU13/06281, by the Generalitat Valenciana under the Prometeo/2009/014 Project Grant ALMA-MATER, and through the EU Projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, grant Ref. 674943). en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof Neural Computing and Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Keyword spotting es_ES
dc.subject Lexicon-based es_ES
dc.subject Smoothing es_ES
dc.subject Out-of-vocabulary es_ES
dc.subject Handwritten text recognition es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Querying out-of-vocabulary words in lexicon-based keyword spotting es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00521-016-2197-8
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/674943/EU/Recognition and Enrichment of Archival Documents/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//PCIN-2015-068/ES/INDEXACION DE MANUSCRITOS HISTORICOS PARA BUSQUEDAS CONTROLADAS POR EL USUARIO/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MECD//FPU13%2F06281/ES/FPU13%2F06281/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Generalitat Valenciana//PROMETEO09%2F2009%2F014/ES/Adaptive learning and multimodality in pattern recognition (Almapater)/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Puigcerver, J.; Toselli, AH.; Vidal, E. (2016). Querying out-of-vocabulary words in lexicon-based keyword spotting. Neural Computing and Applications. 1-10. https://doi.org/10.1007/s00521-016-2197-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://link.springer.com/article/10.1007/s00521-016-2197-8 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 10 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.senia 303254 es_ES
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Educación y Ciencia
dc.contributor.funder Generalitat Valenciana
dc.contributor.funder Ministerio de Ciencia e Innovación
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