DocumentCode :
318017
Title :
A natural stroke-based structural approach to loosely-constrained handwritten Chinese character recognition
Author :
Yeung, Daniel S. ; Fong, H.S.
Author_Institution :
Dept. of Comput., Hong Kong Polytech., Hung Hom, Hong Kong
Volume :
2
fYear :
1997
fDate :
12-15 Oct 1997
Firstpage :
1504
Abstract :
Proposes a natural stroke-based recognition approach to deal with the structural deformation problem in off-line, loosely-constrained handwritten Chinese character recognition. A layered, modular neural network architecture is employed to address problems like shifting, distortion and scaling. Knowledge initialization of the network is speeded up by direct mapping of the character structure knowledge (which is expressed as rules) onto the network. Natural strokes extracted by a fuzzy stroke extractor are input to the recognizer. The proposed rule-mapped network model closely resembles the hierarchical nature of the Chinese character set. 120 categories of handwriting samples are tested, and our recognizer seems to deal with deformations among the samples satisfactorily
Keywords :
character recognition; fuzzy logic; handwriting recognition; knowledge representation; neural net architecture; Chinese character set; character structure knowledge; direct mapping; distortion; fuzzy stroke extractor; knowledge initialization; layered modular neural network architecture; loosely-constrained handwritten Chinese character recognition; natural stroke-based recognition approach; rule-mapped network model; scaling; shifting; structural deformation; Character recognition; Computer architecture; Data preprocessing; Deformable models; Electronic mail; Handwriting recognition; Production; Shape; Testing; Writing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location :
Orlando, FL
ISSN :
1062-922X
Print_ISBN :
0-7803-4053-1
Type :
conf
DOI :
10.1109/ICSMC.1997.638204
Filename :
638204
Link To Document :
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