Unsupervised language model adaptation for handwritten Chinese text recognition
Pattern Recognition
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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.