A Study on Korean and Chinese Character Document Reader Using Neural Network

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In the most studies of Korean character recognition so far, they first classify the characters to 6 types according to their structures and then recognize the characters by identifying their basic components named $'$jaso.$'$ In the study, we propose a method which recognizes the characters without using structure types and is applied to reading documents containing both Korean and Chinese characters. We first classify Korean and Chinese characters by using a modified SOFM model. Then we recognize the characters in each class by using an APC neural network which has the advantage of fast leaning speed and the capablity of additive learning. An experimental result demonstrated the usefulness of the approach with the recognition rate of $\%.$\%.