An evolutionary approach for the generation of diversiform characters using a handwriting model

In pattern recognition, a large number of diversiform characters is necessary to train/test a handwritten character recognition system. In this paper, 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 via-points extracted from actual handwritten characters. The handwriting model can be used to generate a variety of characters by changing via-point information. In this paper, we propose a method for generating a large variety of characters by changing via-point information based on a genetic algorithm, and show that the accuracy of a handwritten character recognition system that uses the characters generated by the proposed method as the training data, is equivalent to that of a system composed by using natural data.