A Study of Discriminative Training for HMM-Based Online Handwritten Chinese/Japanese Character Recognition

We present a study of discriminative training of classifiers using both maximum mutual information (MMI) and minimum classification error (MCE) criteria for online handwritten Chinese/Japanese character recognition based on continuous-density hidden Markov models. It is observed that MCE-trained classifiers can achieve a much higher recognition accuracy than that of MMI-trained ones. Benchmark results of MCE-trained classifiers for simplified Chinese, traditional Chinese and Japanese characters are reported on three recognition tasks with a vocabulary of 9119, 20924, and 12333 characters respectively.