The Character Generation in Handwriting Feature Extraction Using Variational AutoEncoder

Handwriting identification is a method to identify an unknown writer by comparing a known writer's characters with an unknown writer's characters by using the homeostasis of handwriting in the characters. In handwriting identification, improving the accuracy is important in analyzing a large number of characters of the same class. However, a problem with the accuracy of handwriting identification occurs because of lack of characters of the same class. In order to solve this problem, we propose a method of extracting features of handwritten characters from a character class and generating characters of different character class based on the extracted features. It is difficult to extract handwriting features by scientific numerical analysis method, and to generate characters from extracted handwriting features. In this paper, we tried to construct a learning method using Deep Learning to generate a model of the handwriting feature extraction and the handwriting character data generative model. Using the existing neural network model, we tried to apply the generative model of the data of Japanese characters (Hiragana). Because existing models cannot capture complicated character features like Hiragana, we propose a modified method to improve character generation accuracy by adding a convolution model to the existing neural network model. Improvement of character generation accuracy by adding the convolution model was confirmed by using quantitative evaluation method of generated characters.