Evaluation of neural network language models in handwritten Chinese text recognition

Handwritten Chinese text recognition based on over-segmentation and path search integrating contexts has been demonstrated successful, where language models play an important role. Recently, neural network language models (NNLMs) have shown superiority to back-off N-gram language models (BLMs) in handwriting recognition, but have not been studied in Chinese text recognition system. This paper investigates the effects of NNLMs in handwritten Chinese text recognition and compares the performance with BLMs. We trained character-level language models in 3-, 4- and 5- gram on large scale corpora and applied them in text line recognition system. Experimental results on the CASIA-HWDB database show that NNLM and BLM of the same order perform comparably, and the hybrid model by interpolating NNLM and BLM improves the recognition performance significantly.