Highly accurate recognition of printed Korean characters through an improved grapheme recognition method

This paper presents a recognition system which obtains a recognition rate higher than 99% for the printed Korean characters of multifont and multisize. The system consists of 18 neural networks: one for character type classifier and the rest for grapheme recognizers. We recognize a given input by first identifying the character type of the input and then recognizing its constituent graphemes. The problem of this approach is that the other graphemes´ strokes show up in the image area for the grapheme which we try to recognize. These line segments behave like noises and make the training of the neural network difficult. We solved the problem by expanding the input image areas. We observed through experiments that we can keep this high recognition rate even when we increase the number of characters and the number of fonts