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-Ming2 ; Tian, Feng 3; Wang, Hong-An 3; Liu, Cheng-Lin2
发表期刊	PATTERN RECOGNITION
	2014-05-01
卷号	47 期号:5 页码:1904-1916
文章类型	Article
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 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.
关键词	Semi-markov Conditional Random Fields Minimum-risk Training Character String Recognition
WOS标题词	Science & Technology ; Technology
关键词[WOS]	CHINESE CHARACTER-RECOGNITION ; ERROR ; MINIMIZATION ; MODELS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000331667400011
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3085
专题	多模态人工智能系统全国重点实验室_模式分析与学习
作者单位	1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Intelligent Media Tech Res Ctr, Chongqing 400714, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Beijing Key Lab Human Comp Interact, Inst Software, Beijing 100190, Peoples R China
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.

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