1. Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese/Japanese text recognition.
- Author
-
Zhou, Xiang-Dong, Zhang, Yan-Ming, Tian, Feng, Wang, Hong-An, and Liu, Cheng-Lin
- Subjects
- *
HANDWRITING recognition (Computer science) , *MARKOV processes , *CONDITIONAL random fields , *TEXT recognition , *CLASSIFICATION , *MACHINE learning , *COMPUTATIONAL complexity - Abstract
Abstract: 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 TUAT 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. [Copyright &y& Elsevier]
- Published
- 2014
- Full Text
- View/download PDF