Writer Identification for Offline Japanese Handwritten Character using Convolutional Neural Network
Ryosuke Nasuno, Shuichi Arai · 2017
In this paper, we refer to the some kind of features from Convolutional neural network (CNN) for writer identification. We use dataset of Japanese handwritten character, which consists 100 kind of words from each 100 writers. We evaluate two nature of handwritten words: the potential of writer identification for each word and the writer own unique identity. These nature cause a variation of classification accuracy about each handwritten character and about each writer for same word. The former, difference of accuracy is approximately 90% and the feature of each word from CNN have large influence on the accuracy. The latter, difference of accuracy is about 60% and unique writer style can be used to determine authorship of a handwritten document.