Glyph-Based Data Augmentation for Accurate Kanji Character Recognition

In this paper, we address a problem of data augmentation for character recognition. Particularly, we focus on incorporating variation in glyph into data augmentation of character images, which is a simple approach for data augmentation. Generally, existing methods increase data size by distorting images, whereas the proposed method applies noise injection into glyphs, resulting in data with radical variation in glyph. The proposed method exploits public database of glyphs for kanji and augments glyphs by injecting noise into glyphs. Then, we generate images of kanji automatically by deploying stroke images on the augmented glyphs. We carried out experiments for kanji character recognition using augmented data. The results show the effectiveness of the proposed method.