A consideration of writer identification using disentangled features that independent of character classes

Writer identification is one of the active areas of research. It is important to prepare a large number of characters of the same class to improve the accuracy of writer identification. However, it is not always possible to prepare enough characters of the same class. In this case, handwriting examiners compare different classes of characters and analyze using common handwriting parts for each character. However, this is very difficult. Therefore, we assume that handwriting characters written by the same writer have features independent of character classes. In this paper, we propose methods to extract features that are independent of character classes using deep neural networks. We used Conditional Variational AutoEncoder (CVAE) as a learning method. A writer identification experiment shows that these methods can extract independent features of character classes, and extracted features are useful in writer identification. Furthermore, we examined the relationship between human interpretation of character features and accuracy of writer identification by using character features extracted by disentangled feature extraction methods.