Generation of diversiform characters using a computational handwriting model

在模式识别中，手写体字符识别系统需要大量的不同类型的字符进行训练和测试，然而，收集大量的自然样本并不容易。有人建议将字符的人为多样化作为收集各种字符的一种手段。在这项研究中，我们表明，手写模型可以应用到字符的多样化。通过该模型多样化的字符可以用作用于训练/测试目的的字符图像数据库。Wada & Kawato的手写体模型基于最优原则，字符的特征空间包括从实际手写体字符中提取的经由点集。手写体模型可以通过改变视点信息来生成多样化的字符，在本研究中，我们提出了一种基于遗传算法的通过改变视点信息来生成多样化字符的方法，并且我们表明，使用该方法生成的字符作为训练数据的手写体字符识别系统的准确性，与自然数据构成的系统的数据量等价。

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.