Bernoulli HMMs at subword level for handwritten word recognition

This paper presents a handwritten word recogniser based on HMMs at subword level (characters) in which state-emission probabilities are governed by multivariate Bernoulli probability functions. This recogniser works directly with raw binary pixels of the image, instead of conventional, real-valued local features. A detailed experimentation has been carried out by varying the number of states, and comparing the results with those from a conventional system based on continuous (Gaussian) densities. From this experimentation, it becomes clear that the proposed recogniser is much better than the conventional systemWork supported by the EC (FEDER) and the Spanish MEC under the MIPRCV “Consolider Ingenio 2010” research programme (CSD2007-00018), the iTransDoc research project (TIN2006-15694-CO2-01), and the FPU grant AP2005-1840.Giménez Pastor, A.; Juan, A. (2009). Bernoulli HMMs at subword level for handwritten word recognition. En Pattern Recognition and Image Analysis. Springer Verlag (Germany). 497-504. https://doi.org/10.1007/978-3-642-02172-5_64S497504Giménez-Pastor, A., Juan-Císcar, A.: Bernoulli HMMs for Off-line Handwriting Recognition. In: Proc. of the 8th Int. Workshop on Pattern Recognition in Information Systems (PRIS 2008), Barcelona, Spain, pp. 86–91 (June 2008)Günter, S., Bunke, H.: HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components. Pattern Recognition 37, 2069–2079 (2004)Gadea, M.P.: Aportaciones al reconocimiento automático de texto manuscrito. PhD thesis, Dep. de Sistemes Informàtics i Computació, València, Spain. Advisors: Vidal, E., Tosselli, A.H. (October 2007)Juan, A., Vidal, E.: Bernoulli mixture models for binary images. In: Proc. of the 17th Int. Conf. on Pattern Recognition (ICPR 2004), Cambridge, UK, vol. 3 (August 2004)Marti, U.V., Bunke, H.: The IAM-database: an English sentence database for offline handwriting recognition.  5(1), 39–46 (2002)Rabiner, L., Juang, B.-H.: Fundamentals of speech recognition. Prentice-Hall, Englewood Cliffs (1993)Romero, V., Giménez, A., Juan, A.: Explicit Modelling of Invariances in Bernoulli Mixtures for Binary Images. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS (LNAI), vol. 4477, pp. 539–546. Springer, Heidelberg (2007)Young, S., et al.: The HTK Book. Cambridge University Engineering Department (1995