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%.