Manga Text Detection with Manga-Specific Data Augmentation and Its Applications on Emotion Analysis.

We especially target at detecting text in atypical font styles and in cluttered background for Japanese comics (manga). To enable the detection model to detect atypical text, we augment training data by the proposed manga-specific data augmentation. A generative adversarial network is developed to generate atypical text regions, which are then blended into manga pages to largely increase the volume and diversity of training data. We verify the importance of manga-specific data augmentation. Furthermore, with the help of manga text detection, we fuse global visual features and local text features to enable more accurate emotion analysis.