Puigcerver, JoanToselli, Alejandro HéctorVidal, Enrique2017-06-092017-06-092016-020941-0643https://riunet.upv.es/handle/10251/82643The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2197-8[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.Reserva de todos los derechosKeyword spottingLexicon-basedSmoothingOut-of-vocabularyHandwritten text recognitionLENGUAJES Y SISTEMAS INFORMATICOSArtículo10.1007/s00521-016-2197-8Abierto