A study on the use of CDHMM for large vocabulary offline recognition of handwritten chinese characters

We've been investigating how to use Gaussian mixture continuous-density hidden Markov, models (CDHMMs) for handwritten Chinese character modeling and recognition. We've identified and developed a set of techniques that can be used to construct a practical CDHMM-based offline recognition system for a large vocabulary of handwritten Chinese characters. We have reported elsewhere the key techniques that contribute to the high recognition accuracy. In this paper we describe how to make our recognizer compact without sacrificing too much of the recognition accuracy. We also report the results of a series of experiments that were performed to help us make a good decision when we face several design choices.