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Title:
Minimum Risk Training for Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields
Author: Zhou, Xiang-Dong ; Tian, Feng ; Liu, Cheng-Lin ; Wang, Hong-An
Conference Name: 12th International Conference on Document Analysis and Recognition (ICDAR)
Conference Date: AUG 25-28, 2013
Issued Date: 2013
Conference Place: Washington, DC
Publish Place: IEEE
Indexed Type: CPCI
ISSN: 1520-5363
Department: [Zhou, Xiang-Dong; Tian, Feng; Wang, Hong-An] Chinese Acad Sci, Beijing Key Lab Human Comp Interact, Inst Software, Beijing 100190, Peoples R China.
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, in which the misclassification costs are not equal, but different depending on the hypothesis and the ground-truth. The proposed method is lattice-based, i.e., the hypothesis space is the entire lattice on which the semi-CRF is defined. Experimental results on two online handwriting databases: CASIA-OLHWDB and TUAT Kondate demonstrate that minimum-risk training can yield superior string recognition rates compared to MAP training.
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, in which the misclassification costs are not equal, but different depending on the hypothesis and the ground-truth. The proposed method is lattice-based, i.e., the hypothesis space is the entire lattice on which the semi-CRF is defined. Experimental results on two online handwriting databases: CASIA-OLHWDB and TUAT Kondate demonstrate that minimum-risk training can yield superior string recognition rates compared to MAP training.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16509
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Zhou, Xiang-Dong,Tian, Feng,Liu, Cheng-Lin,et al. Minimum Risk Training for Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields[C]. 见:12th International Conference on Document Analysis and Recognition (ICDAR). Washington, DC. AUG 25-28, 2013.
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