Deep Neural Network Based Hidden Markov Model For Offline Handwritten Chinese Text Recognition

This paper proposes a novel segmentation-free approach using deep neural network based hidden Markov model (DNN-HMM) for offline handwritten Chinese text recognition. In the general Bayesian framework, three key issues are comprehensively investigated, namely feature extraction, character modeling, and language modeling. First, as for the feature extraction on the basis of each frame or sliding window, the gradient-based features are extracted for the DNN-based classifier. Second, the text line is sequentially modeled by HMMs with each representing one character class. Meanwhile the DNNbased classifier is adopted to calculate the posterior probability of all HMM states. Finally, the character n-gram language model is integrated with the DNN-HMM character model for the Bayesian decision. The experiments on the ICDAR 2013 competition task of CASIA-HWDB database show that the proposed approach can achieve the best published recognition results to our knowledge, yielding a character error rate (CER) of 6.50%, which significantly outperforms the previously best reported oversegmentation approach (with a CER of 9.25%) and the segmentation- free approach using multidimensional longshort term memory recurrent neural network (MDLSTM-RNN) approach (with a CER of 10.6%).