on-line handwritten chinese character recognition based on inter-radical stochastic context-free grammar

ISCAS OpenIR

	on-line handwritten chinese character recognition based on inter-radical stochastic context-free grammar
	Ma Long-Long; Wu Jian
	2012
会议名称	2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
会议录名称	Proceedings of the International Joint Conference on Neural Networks
页码	-
会议日期	June 10, 2012 - June 15, 2012
会议地点	Brisbane, QLD, Australia
收录类别	EI
ISBN	9781467314909
部门归属	(1) National Engineering Research Center of Fundamental Software Institute of Software Chinese Academy of Sciences Beijing China
摘要	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.; 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.
关键词	Context Free Grammars Dynamic Positioning Forestry Neural Networks Trees (Mathematics)
主办者	IEEE Computational Intelligence Society (CIS); International Neural Network Society (INNS)
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15765
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Ma Long-Long,Wu Jian. on-line handwritten chinese character recognition based on inter-radical stochastic context-free grammar[C],2012:-.