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Neural Computing and Applications

, Volume 28, Issue 9, pp 2373–2382 | Cite as

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

  • Joan Puigcerver
  • Alejandro H. Toselli
  • Enrique Vidal
IBPRIA 2015
  • 324 Downloads

Abstract

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.

Keywords

Keyword spotting Lexicon-based Smoothing Out-of-vocabulary Handwritten text recognition 

Notes

Acknowledgments

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 ALMAMATER, and through the EU Projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, grant Ref. 674943).

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.PRHLT Research CenterUniversitat Politècnica de ValènciaValenciaSpain

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