Improving Handwritten Chinese Text Recognition by Unsupervised Language Model Adaptation

This paper investigates the effects of unsupervised language model adaptation (LMA) in handwritten Chinese text recognition. For no prior information of recognition text is available, we use a two-pass recognition strategy. In the first pass, the generic language model (LM) is used to get a preliminary result, which is used to choose the best matched LMs from a set of pre-defined domains, then the matched LMs are used in the second pass recognition. Each LM is compressed to a moderate size via the entropy-based pruning, tree-structure formatting and fewer-byte quantization. We evaluated the LMA for five LM types, including both character-level and word-level ones. Experiments on the CASIA-HWDB database show that language model adaptation improves the performance for each LM type in all domains. The documents of ancient domain gained the biggest improvement of character-level correct rate of 5.87 percent up and accurate rate of 6.05 percent up.