Cross-learning in analytic word recognition without segmentation

Abstract. In this paper a method for analytic handwritten word recognition based on causal Markov random fields is described. The word models are hmms where each state corresponds to a letter modeled by a nshp-hmm (Markov field). The word models are built dynamically. Training is operated using Baum-Welch algorithm where the parameters are reestimated on the generated word models. The segmentation is unnecessary: the system determines itself during training the best repartition of the information within the letter models. First experiments on two real databases of French check amount words give very encouraging results up to 86% for recognition without rejection.

[1]  M.A. Ozdil,et al.  Optical character recognition without segmentation , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[2]  C. Coisy,et al.  Analytic word recognition without segmentation based on Markov random fields , 2004 .

[3]  Jung-Hsien Chiang,et al.  Handwritten word recognition with character and inter-character neural networks , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Horst Bunke,et al.  Lexicon reduction in an framework based on quantized feature vectors , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[5]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[6]  Rolf-Dieter Bippus.  1-dimensional and pseudo 2-dimensional HMMs for the recognition of German literal amounts , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[7]  Seiichi Uchida,et al.  An Efficient Two-Dimensional Warping Algorithm , 1999 .

[8]  Seong-Whan Lee,et al.  An HMMRF-based statistical approach for off-line handwritten character recognition , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[9]  Michel Gilloux,et al.  A hybrid radial basis function network/hidden Markov model handwritten word recognition system , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[10]  Jian Zhou,et al.  Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  George Saon.  Modèles markoviens uni- et bidimensionnels pour la reconnaissance de l'écriture manuscrite hors-ligne. (One and two-dimensional Markov models for off-line handwriting recognition) , 1997 .

[12]  Venu Govindaraju,et al.  Reading handwritten US census forms , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[13]  G. Saon,et al.  Off-line handwritten word recognition using a mixed HMM-MRF approach , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[14]  Seiichi Uchida,et al.  Handwritten character recognition using piecewise linear two-dimensional warping , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[15]  Fumitaka Kimura,et al.  Handwritten word recognition using lexicon free and lexicon directed word recognition algorithms , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[16]  Anthony J. Robinson,et al.  An Off-Line Cursive Handwriting Recognition System , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Robert Sabourin,et al.  An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[19]  Oscar E. Agazzi,et al.  Hidden markov model based optical character recognition in the presence of deterministic transformations , 1993, Pattern Recognit..

[20]  Atul Negi,et al.  A correspondence based approach to segmentation of cursive words , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.