HMM model for online handwritten Chinese character recognition describing the correlations between segments

This paper proposes a new type of hidden Markov model called the attributed relation hidden Markov model (ARHMM) which combines the advantages of the traditional HMM and the attributed relation graph (ARG). The model uses new observations directly describing the correlations between the states with the original observations, while preserving the HMM mathematical structure. The modified ARHMM learning methods and recognition algorithms are presented. In an experiment with online handwritten Chinese character recognition, ARHMM was utilized to describe the correlations between the character segments. The results demonstrate that this model performed much better than a traditional HMM for all samples with different qualities.