期刊
PATTERN RECOGNITION
卷 47, 期 5, 页码 1904-1916出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2013.12.002
关键词
Semi-Markov conditional random fields; Minimum-risk training; Character string recognition
资金
- National Natural Science Foundation of China (NSFC) [61273269, 60933010, 61203296, 61232013]
- Chongqing Science & Technology Commission [cstc2013yykfB0233]
Semi-Markov conditional random fields (semi-CRFs) are usually trained with maximum a posteriori (MAP) criterion which adopts the 0/1 cost for measuring the loss of misclassification. In this paper, based on our previous work on handwritten Chinese/Japanese text recognition (HCTR) using semi-CRFs, we propose an alternative parameter learning method by minimizing the risk on the training set, which has unequal misclassification costs depending on the hypothesis and the ground-truth. Based on this framework, three non-uniform cost functions are compared with the conventional 0/1 cost, and training data selection is incorporated to reduce the computational complexity. In experiments of online handwriting recognition on databases CASIA-OLHWDB and THAT Kondate, we compared the performances of the proposed method with several widely used learning criteria, including conditional log-likelihood (CLL), softmax-margin (SMM), minimum classification error (MCE), large-margin MCE (LM-MCE) and max-margin (MM). On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition. (C) 2013 Elsevier Ltd. All rights reserved.
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