Generation of diversiform characters using a computational handwriting model

在模式识别中，训练/测试手写字符识别系统需要大量的各种字符，但要采集大量的自然样本并非易事。字符的人工多样化被认为是收集各种字符的一种手段。在这项研究中，我们证明了笔迹模型可以应用于字符的多样化。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的数据库。WADA和Kawato的手写模型基于最优原则，字符的特征空间包括从实际手写字符中提取的视点集合。手写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产生多样化字符的方法，并证明了使用该方法生成的字符作为训练数据的手写字符识别系统的准确率与使用自然数据组成的系统的准确率是相当的.

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.