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Title:
on-line handwritten chinese character recognition based on inter-radical stochastic context-free grammar
Author: Ma Long-Long ; Wu Jian
Source: Proceedings of the International Joint Conference on Neural Networks
Conference Name: 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Conference Date: June 10, 2012 - June 15, 2012
Issued Date: 2012
Conference Place: Brisbane, QLD, Australia
Keyword: Context free grammars ; Dynamic positioning ; Forestry ; Neural networks ; Trees (mathematics)
Indexed Type: EI
ISBN: 9781467314909
Department: (1) National Engineering Research Center of Fundamental Software Institute of Software Chinese Academy of Sciences Beijing China
Sponsorship: IEEE Computational Intelligence Society (CIS); International Neural Network Society (INNS)
Abstract: This paper presents a new radical-based recognition method for online handwritten Chinese characters focusing on their hierarchical structure. Inter-radical stochastic context-free grammar (SCFG) is introduced to represent the character generation process where radicals as structure elements. Inter-radical SCFG combines the radical shape likelihood with the relative position likelihood between radicals/meta-radicals. The character pattern is over-segmented by three-layer nested pre-segmentation. Character-radical dictionaries of all character classes are unified into several big tree structures where character-parts (sub-structures) are shared by different character classes. Combining inter-radical SCFG with tree structural character-radical dictionaries, the optimal radical segmentation and recognition result is obtained during hierarchical dynamic programming (DP) search. We have implemented the method to Chinese characters of left-right and up-down structures. Experimental results on a sample set of 5,773 character classes consisting of 1,149 radicals show the proposed method is comparable to our previous method. © 2012 IEEE.
English Abstract: This paper presents a new radical-based recognition method for online handwritten Chinese characters focusing on their hierarchical structure. Inter-radical stochastic context-free grammar (SCFG) is introduced to represent the character generation process where radicals as structure elements. Inter-radical SCFG combines the radical shape likelihood with the relative position likelihood between radicals/meta-radicals. The character pattern is over-segmented by three-layer nested pre-segmentation. Character-radical dictionaries of all character classes are unified into several big tree structures where character-parts (sub-structures) are shared by different character classes. Combining inter-radical SCFG with tree structural character-radical dictionaries, the optimal radical segmentation and recognition result is obtained during hierarchical dynamic programming (DP) search. We have implemented the method to Chinese characters of left-right and up-down structures. Experimental results on a sample set of 5,773 character classes consisting of 1,149 radicals show the proposed method is comparable to our previous method. © 2012 IEEE.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15765
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Ma Long-Long,Wu Jian. on-line handwritten chinese character recognition based on inter-radical stochastic context-free grammar[C]. 见:2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012. Brisbane, QLD, Australia. June 10, 2012 - June 15, 2012.
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