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Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields
文献类型：期刊论文
| 作者 | Zhou, Xiang-Dong ; Wang, Da-Han ; Tian, Feng ; Liu, Cheng-Lin ; Nakagawa, Masaki |
| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2013 |
| 卷号 | 35期号:10页码:2413-2426 |
| 关键词 | Character string recognition semi-Markov conditional random field lattice pruning beam search |
| ISSN号 | 0162-8828 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000323175200008 |
| 公开日期 | 2014-12-16 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16723]  |
| 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,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. |
入库方式： OAI收割
来源：软件研究所
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