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| Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese/Japanese text recognition | |
| Zhou, Xiang-Dong (1); Zhang, Yan-Ming (2); Tian, Feng (3); Wang, Hong-An (3); Liu, Cheng-Lin (2); Zhou, X.-D.(zhouxiangdong@cigit.ac.cn) | |
| 2014 | |
| Source | Pattern Recognition
 |
| ISSN | 313203 |
| Volume | 47Issue:5Pages:1904-1916 |
| English 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. © 2013 Elsevier Ltd. All rights reserved.; 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. © 2013 Elsevier Ltd. All rights reserved. |
| Indexed Type | SCI ; EI |
| Keyword | Semi-markov Conditional Random Fields Minimum-risk Training Character String Recognition |
| Department | (1) Intelligent Media Technique Research Center, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; (2) National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguan East Road, Beijing 100190, China; (3) Beijing Key Lab of Human-Computer Interaction, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China |
| Language | 英语 |
| WOS ID | WOS:000331667400011 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16721 |
| Collection | 中国科学院软件研究所 |
| Corresponding Author | Zhou, X.-D.(zhouxiangdong@cigit.ac.cn) |
| Recommended Citation GB/T 7714 | Zhou, Xiang-Dong ,Zhang, Yan-Ming ,Tian, Feng ,et al. Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese/Japanese text recognition[J]. Pattern Recognition,2014,47(5):1904-1916. |
| APA | Zhou, Xiang-Dong ,Zhang, Yan-Ming ,Tian, Feng ,Wang, Hong-An ,Liu, Cheng-Lin ,&Zhou, X.-D..(2014).Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese/Japanese text recognition.Pattern Recognition,47(5),1904-1916. |
| MLA | Zhou, Xiang-Dong ,et al."Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese/Japanese text recognition".Pattern Recognition 47.5(2014):1904-1916. |
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