Online handwritten English word recognition based on cascade connection of character HMMs

In this paper, a cascade connection hidden Markov model (CCHMM) method for online English word recognition is proposed. This model, which allows state transition, skip and duration, extends the way of HMM pattern description of handwriting English words. According to the statistic probabilities, the behavior of handwriting curve may be depicted more precisely. The Viterbi algorithm for the cascade connection model may be applied after the whole sample series of a word is input. Compared with the method of creating models for each word in lexicon, this method gives a faster recognition speed. Experiments show that CCHMM approach could obtain 89.26% recognition rate for the first candidate, while the combination of character and ligature HMM method's first candidate is 82.34%.

[1]  Liu Jia.  A HMM Based On-line Chinese Character Recognition System and Improved Training Algorithm , 2001 .

[2]  Reinhold Häb-Umbach,et al.  Signal representations for hidden Markov model based online handwriting recognition , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Jin Hyung Kim,et al.  Network-based approach to online cursive script recognition , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Gerhard Rigoll,et al.  Advanced state clustering for very large vocabulary HMM-based on-line handwriting recognition , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[5]  Jerome R. Bellegarda,et al.  On-line handwriting recognition using continuous parameter hidden Markov models , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  Richard M. Schwartz,et al.  On-line cursive handwriting recognition using speech recognition methods , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.