Manga109 dataset and creation of metadata

By Azuma Fujimoto, Toru Ogawa, Kazuyoshi Yamamoto, Yusuke Matsui, Toshihiko Yamasaki, Kiyoharu Aizawa. 2016

167 citations recorded by the source corpus.

We have created Manga109, a dataset of a variety of 109 Japanese comic books publicly available for use for academic purposes. This dataset provides numerous comic images but lacks the annotations of elements in the comics that are necessary for use by machine learning algorithms or evaluation of methods. In this paper, we present our ongoing project to build metadata for Manga109. We first define the metadata in terms of frames, texts and characters. We then present our web-based software for efficiently creating the ground truth for these images. In addition, we provide a guideline for the annotation with the intent of improving the quality of the metadata.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Salman Hameed Khan
2. Nick Barnes
3. Saeed Anwar
4. Jian Chen
5. Yong Guo
6. Zhenhao He
7. Yongsheng Luo
8. Jin Huang

Citations in the corpus

• Hierarchical Neural Architecture Search for Single Image Super-Resolution (2020, methodology): “To show the effectiveness of out method, we conduct comparisons on five benchmark datasets, including Set5 [36], Set14 [37], BSD100 [38], Urban100 [39], and Manga109 [40].”
• Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study (2019, unknown): “Therefore, many newer and larger datasets are presented for image and video restoration, such as DIV2K [54] and MANGA109 [55] for image super-resolution, PolyU [56] and Darmstadt [57] for denoising, RawInDark [58] and LOL dataset [59] for low light enhancement, HazeRD [60], OHAZE [61] and IHAZE [62]”
• Deep Learning for Image Super-Resolution: A Survey (2019, unknown): “Manga109 [45] 109 (826, 1169) 966, 011 PNG manga volume”
• Real-World Single Image Super-Resolution: A Brief Review (2021, methodology): “For the training and testing of SISR models, the widely used datasets include DIV2K [128], BSDS500 [129], T91 [53], Set5 [130], Set14 [54], Urban100 [50], Manga109 [131], etc .”
• A Deep Journey into Super-resolution (2019, methodology): “In this section, we compare the state-of-the-art algorithms on publicly available benchmark datasets which include Set5 [88], Set14 [89], BSD100 [90], Urban100 [91], DIV2K [50] and Manga109 [92].”
• Densely Residual Laplacian Super-Resolution (2019, methodology): “Then we test the model on five publicly available super-resolution dataset, namely, SET5 [46], SET14 [47], URBAN100 [45], B100 [48], and MANGA109 [43].”