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Title:
Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields
Author: Zhou, Xiang-Dong ; Wang, Da-Han ; Tian, Feng ; Liu, Cheng-Lin ; Nakagawa, Masaki
Keyword: Character string recognition ; semi-Markov conditional random field ; lattice pruning ; beam search
Source: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Issued Date: 2013
Volume: 35, Issue:10, Pages:2413-2426
Indexed Type: SCI
Department: [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.
Abstract: 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.
English Abstract: 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.
Language: 英语
WOS ID: WOS:000323175200008
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16723
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
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,2013-01-01,35(10):2413-2426.
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