RECOGNITION OF LARGE-SET PRINTED HANGUL (KOREAN SCRIPT) BY 2-STAGE BACKPROPAGATION NEURAL CLASSIFIER

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A two-stage neural network classifier is described which practically recognizes printed Hangul (Korean script). This classifier is composed of a type classification network and six recognition networks. The former classifies input character images into one of the six types by their overall structure, and then the latter classify them into character code. Furthermore, a training scheme including systematic noises is introduced for improving the generalization capability of the networks. Experiments are conducted with the most frequently used 990 printed Hangul syllables. By the noise included training, the recognition rate amounts to 98.28%, which is better than that of the conventional backpropagation learning. A comparison with a statistical classifier and an analysis of generalization capability confirm the relative superiority of the proposed classification method.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
1992-11
Language
English
Article Type
Article
Citation

PATTERN RECOGNITION, v.25, no.11, pp.1353 - 1360

ISSN
0031-3203
URI
http://hdl.handle.net/10203/12161
Appears in Collection
CS-Journal Papers(저널논문)
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