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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References
Fischer, A., Keller, A., Frinken, V., Bunke, H.: Lexicon-free handwritten word spotting using character HMMs. Pattern Recogn. Lett. 33(7), 934–942 (2012). special Issue on Awards from ICPR 2010
Frinken, V., Fischer, A., Manmatha, R., Bunke, H.: A novel word spotting method based on recurrent neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 211–224 (2012)
Kneser, R., Ney, H.: Improved backing-off for N-gram language modeling. In: International Conference on Acoustics. Speech and Signal Processing (ICASSP 1995), vol. 1, pp. 181–184. IEEE Computer Society, Los Alamitos (1995)
Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Puigcerver, J., Toselli, A.H., Vidal, E.: Word-graph and character-lattice combination for KWS in handwritten documents. In: 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 181–186 (2014)
Puigcerver, J., Toselli, A.H., Vidal, E.: Word-graph-based handwriting keyword spotting of out-of-vocabulary queries. In: 22nd International Conference on Pattern Recognition (ICPR), pp. 2035–2040 (2014)
Robertson, S.: A new interpretation of average precision. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 689–690. ACM, New York (2008)
Rodriguez-Serrano, J.A., Perronnin, F.: Handwritten word-spotting using hidden markov models and universal vocabularies. Pattern Recogn. 42(9), 2106–2116 (2009). http://www.sciencedirect.com/science/article/pii/S0031320309000673
Shang, H., Merrettal, T.: Tries for approximate string matching. IEEE Transac. Knowl. Data Eng. 8(4), 540–547 (1996)
Toselli, A.H., Vidal, E.: Fast HMM-filler approach for key word spotting in handwritten documents. In: Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 501–505 (2013)
Toselli, A.H., Vidal, E., Romero, V., Frinken, V.: Word-graph based keyword spotting and indexing of handwritten document images. Universitat Politcnica de Valncia, Technical report (2013)
Woodland, P., Leggetter, C., Odell, J., Valtchev, V., Young, S.: The 1994 HTK large vocabulary speech recognition system. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1995), vol. 1, pp. 73–76, May 1995
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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