Abstract
Lexicon-based handwritten text keyword spotting (KWS) has proven to be a very fast and accurate alternative to lexicon-free methods. Nevertheless, since lexicon-based KWS methods rely on a predefined vocabulary, fixed in the training phase, they perform poorly for any query keyword that was not included in it (i.e. out-of-vocabulary keywords). This turns the KWS system useless for that particular type of queries. In this paper, we present a new way of smoothing the scores of OOV keywords, and we compare it with previously published alternatives on different data sets.
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Acknowledgments
This work was partially supported by the Spanish MEC under FPU grant FPU13/06281 and under the STraDA research project (TIN2012-37475-C02-01), by the Generalitat Valenciana under the grant Prometeo/2009/014, and through the EU 7th Framework Programme grant tranScriptorium (Ref: 600707).
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Puigcerver, J., Toselli, A.H., Vidal, E. (2015). A New Smoothing Method for Lexicon-Based Handwritten Text Keyword Spotting. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_3
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DOI: https://doi.org/10.1007/978-3-319-19390-8_3
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