A hierarchical neural network which recognizes printed Hangul (Korean) characters is proposed. This system 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 the latter further classify them into character code. A training scheme including systematic noises is introduced for improving the generalization capabilities of the networks. With the noise-included training, the recognition rate is up to 98.28%, which is superior to the conventional back-propagation network. The neural network approach is very reasonable compared to statistical classifiers and an analysis of generalization capability demonstrates acceptable performance.
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