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Exploring MPE/MWE Training for Chinese Handwriting Recognition

The HMM-based segmentation-free strategy for Chinese handwriting recognition has the merit that the model parameters can be trained with text line samples without annotation of character boundaries. However, the recognition performance has been limited to the general maximum likelihood estimation framework. In this paper, we investigate the discriminative training framework based on MPE/MWE criteria in the context of Chinese handwriting recognition for the first time. It optimizes a objective function that is a smooth measure of recognition error. Then EBW procedure is used to solve such criteria. Some key issues for robust MPE/MWE training are explored. We reveal that MPE/MWE requires more training samples, however, Chinese handwriting recognition poses severe data sparsity problem. We explore the sample synthesizing to help the training process. Experiments are conducted on Chinese handwriting database and the effectiveness of MPE/MWE training is manifested. In particular, at least 28% error reduction of recognition rates is observed in MPE/MWE training with 50 copies of synthetic sample when big ram is used to approximate the language model.