Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields

ISCAS OpenIR

	Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields
	Zhou, Xiang-Dong; Wang, Da-Han; Tian, Feng; Liu, Cheng-Lin; Nakagawa, Masaki
	2013
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
卷号	35 期号:10 页码:2413-2426
摘要	This paper proposes a method for handwritten Chinese/Japanese text (character string) recognition based on semi-Markov conditional random fields (semi-CRFs). The high-order semi-CRF model is defined on a lattice containing all possible segmentation-recognition hypotheses of a string to elegantly fuse the scores of candidate character recognition and the compatibilities of geometric and linguistic contexts by representing them in the feature functions. Based on given models of character recognition and compatibilities, the fusion parameters are optimized by minimizing the negative log-likelihood loss with a margin term on a training string sample set. A forward-backward lattice pruning algorithm is proposed to reduce the computation in training when trigram language models are used, and beam search techniques are investigated to accelerate the decoding speed. We evaluate the performance of the proposed method on unconstrained online handwritten text lines of three databases. On the test sets of databases CASIA-OLHWDB (Chinese) and TUAT Kondate (Japanese), the character level correct rates are 95.20 and 95.44 percent, and the accurate rates are 94.54 and 94.55 percent, respectively. On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition.; This paper proposes a method for handwritten Chinese/Japanese text (character string) recognition based on semi-Markov conditional random fields (semi-CRFs). The high-order semi-CRF model is defined on a lattice containing all possible segmentation-recognition hypotheses of a string to elegantly fuse the scores of candidate character recognition and the compatibilities of geometric and linguistic contexts by representing them in the feature functions. Based on given models of character recognition and compatibilities, the fusion parameters are optimized by minimizing the negative log-likelihood loss with a margin term on a training string sample set. A forward-backward lattice pruning algorithm is proposed to reduce the computation in training when trigram language models are used, and beam search techniques are investigated to accelerate the decoding speed. We evaluate the performance of the proposed method on unconstrained online handwritten text lines of three databases. On the test sets of databases CASIA-OLHWDB (Chinese) and TUAT Kondate (Japanese), the character level correct rates are 95.20 and 95.44 percent, and the accurate rates are 94.54 and 94.55 percent, respectively. On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition.
收录类别	SCI
关键词	Character String Recognition Semi-markov Conditional Random Field Lattice Pruning Beam Search
部门归属	[Zhou, Xiang-Dong; Tian, Feng] Chinese Acad Sci, Beijing Key Lab Human Comp Interact, Inst Software, Beijing 100190, Peoples R China. [Tian, Feng] Chinese Acad Sci, State Key Lab Comp Sci, Beijing 100190, Peoples R China. [Wang, Da-Han; Liu, Cheng-Lin] Chinese Acad Sci, NLPR, Inst Automat, Beijing 100190, Peoples R China. [Nakagawa, Masaki] Tokyo Univ Agr & Technol, Dept Comp & Informat Sci, Koganei, Tokyo 1848588, Japan.
语种	英语
WOS记录号	WOS:000323175200008
引用统计
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16723
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Zhou, Xiang-Dong,Wang, Da-Han,Tian, Feng,et al. Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2013,35(10):2413-2426.
APA	Zhou, Xiang-Dong,Wang, Da-Han,Tian, Feng,Liu, Cheng-Lin,&Nakagawa, Masaki.(2013).Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,35(10),2413-2426.
MLA	Zhou, Xiang-Dong,et al."Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 35.10(2013):2413-2426.