Generation of diversiform characters using a computational handwriting model

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In pattern recognition, a large number of diversiform characters is necessary to train / test a handwritten character recognition system: However, it is not easy to collect a large number of natural samples. The artificial diversification of characters has been suggested as one means of collecting a variety of characters. In this research, we show that a handwriting model can be applied to the diversification of characters. The characters diversified by the model can be used as a database of character images for training / testing purposes. Wada ＆ Kawato's handwriting model is based on an optimal principle and the feature space of the characters includes sets of viapoints extracted from actual handwritten characters. The handwriting model can be used to generate diversiform characters by changing viapoint information.In this research, we propose a method for generating a diversification of characters by changing viapoint information based on a genetic algorithm and we show that the accuracy of a handwritten character recognition system that uses the character generated by the proposed method as the training data, is equivalent to that of a system composed by using natural dat.