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

Abstract

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.