Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese/Japanese text recognition

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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)
刊名	Pattern Recognition
	2014
卷号	47 期号:5 页码:1904-1916
关键词	Semi-Markov conditional random fields Minimum-risk training Character string recognition
ISSN号	313203
通讯作者	Zhou, X.-D.(zhouxiangdong@cigit.ac.cn)
中文摘要	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.
英文摘要	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.
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000331667400011
公开日期	2014-12-16
内容类型	期刊论文
源URL	[http://ir.iscas.ac.cn/handle/311060/16721]
专题	软件研究所_软件所图书馆_期刊论文
推荐引用方式 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 .(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.