Abstract
Handwritten Text Recognition is a problem that has gained attention in the last years mainly due to the interest in the transcription of historical documents. However, the automatic transcription of handwritten documents is not error free and human intervention is typically needed to correct the results of such systems. This interactive scenario demands real-time response. In this paper, we present a study comparing how different pruning techniques affect the performance of two freely available decoding systems, HTK and iATROS. These two systems are based on Hidden Markov Models and n-gram language models. However, while HTK only considers 2-gram language models, iATROS works with n-grams of any order. In this paper, we also carried out a study about how the use of n-grams of size greater than two can enhance results over 2-grams. Experiments are reported with the publicly available ESPOSALLES database.
This work was partially supported by the Spanish MEC under the STraDA research project (TIN2012-37475-C02-01), the MITTRAL (TIN2009-14633-C03-01) project, the FPU scholarship AP2010-0575, 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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References
Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press (1998)
Kavallieratou, E., Stamatatos, E.: Improving the quality of degraded document images. In: Proc. of 2nd IEEE Int. Conf. on Document Image Analysis for Libraries, Washington DC, USA, pp. 340–349 (2006)
Kneser, R., Ney, H.: Improved backing-off for n-gram language modeling. Proc. of the ICASSP 1995, pp. 181–184 (1995)
Luján-Mares, M., Tamarit, V., Alabau, V., Martínez-Hinarejos, C.D.: i Gadea, M.P., Sanchis, A., Toselli, A.H.: iATROS: A speech and handwritting recognition system. In: V Jornadas en Tecnologías del Habla, pp. 75–78 (2008)
Ney, H., Mergel, D., Noll, A., Paeseler, A.: Data driven search organization for continuous speech recognition. Trans. Sig. Proc. 40(2), 272–281 (1992)
Romero, V., Pastor, M., Toselli, A.H., Vidal, E.: Criteria for handwritten off-line text size normalization. In: Proc. of the 5th Int. Conf. on Visualization, Imaging and Image, Spain (2006)
Romero, V., Fornés, A., Serrano, N., Sánchez, J.A., Toselli, A.H., Frinken, V., Vidal, E., Lladós, J.: The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition. Pattern Recognition (in press, 2013)
Steinbiss, V., Tran, B.H., Ney, H.: Improvements in beam search. In: ICSLP (1994)
Toselli, A.H., et al.: Integrated Handwriting Recognition and Interpretation using FS Models. Int. Journal on Pat. Rec. and Artif. Intel. 18(4), 519–539 (2004)
Toselli, A.H., Romero, V., Pastor, M., Vidal, E.: Multimodal interactive transcription of text images. Pattern Recognition 43(5), 1814–1825 (2010)
Vidal, E., Rodríguez, L., Casacuberta, F., García-Varea, I.: Interactive pattern recognition. In: Popescu-Belis, A., Renals, S., Bourlard, H. (eds.) MLMI 2007. LNCS, vol. 4892, pp. 60–71. Springer, Heidelberg (2008)
Young, S.J., Kershaw, D., Odell, J., Ollason, D., Valtchev, V., Woodland, P.: The HTK Book Version 3.4. Cambridge University Press (2006)
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Martín-Albo, D., Romero, V., Vidal, E. (2013). An Experimental Study of Pruning Techniques in Handwritten Text Recognition Systems. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_66
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DOI: https://doi.org/10.1007/978-3-642-38628-2_66
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