Building a Manga Dataset "Manga109" with Annotations for Multimedia Applications
This dataset addresses the problem of limited multimodal data for manga researchers and developers, enabling applications like detection and retrieval, though it is incremental as it applies existing methods to new data.
The authors tackled the lack of a proper dataset for manga in deep learning by building Manga109, a dataset of 109 Japanese comic books with over 500k annotations, and made it publicly available for academic and industrial use.
Manga, or comics, which are a type of multimodal artwork, have been left behind in the recent trend of deep learning applications because of the lack of a proper dataset. Hence, we built Manga109, a dataset consisting of a variety of 109 Japanese comic books (94 authors and 21,142 pages) and made it publicly available by obtaining author permissions for academic use. We carefully annotated the frames, speech texts, character faces, and character bodies; the total number of annotations exceeds 500k. This dataset provides numerous manga images and annotations, which will be beneficial for use in machine learning algorithms and their evaluation. In addition to academic use, we obtained further permission for a subset of the dataset for industrial use. In this article, we describe the details of the dataset and present a few examples of multimedia processing applications (detection, retrieval, and generation) that apply existing deep learning methods and are made possible by the dataset.