# Building a Manga Dataset "Manga109" with Annotations for Multimedia Applications

arXiv: https://arxiv.org/abs/2005.04425 · HTML: https://mlsanity.com/paper/2005.04425/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2020.

## Built on by (most cited first, 30 of 38)

- [Transformer for Single Image Super-Resolution](https://mlsanity.com/paper/2108.11084/) (2021)
- [The Manga Whisperer: Automatically Generating Transcriptions for Comics](https://mlsanity.com/paper/2401.10224/) (2024)
- [DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation](https://mlsanity.com/paper/2412.07589/) (2024)
- [Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review](https://mlsanity.com/paper/2308.00247/) (2023)
- [Manga109Dialog: A Large-scale Dialogue Dataset for Comics Speaker Detection](https://mlsanity.com/paper/2306.17469/) (2023)
- [COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Texts](https://mlsanity.com/paper/2207.04675/) (2022)
- [Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names](https://mlsanity.com/paper/2408.00298/) (2024)
- [USB: Universal-Scale Object Detection Benchmark](https://mlsanity.com/paper/2103.14027/) (2021)
- [Identity-Aware Semi-Supervised Learning for Comic Character Re-Identification](https://mlsanity.com/paper/2308.09096/) (2023)
- [MangaUB: A Manga Understanding Benchmark for Large Multimodal Models](https://mlsanity.com/paper/2407.19034/) (2024)
- [MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding](https://mlsanity.com/paper/2505.20298/) (2025)
- [Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review](https://mlsanity.com/paper/2111.12146/) (2021)
- [A Challenging Benchmark of Anime Style Recognition](https://mlsanity.com/paper/2204.14034/) (2022)
- [From Panels to Prose: Generating Literary Narratives from Comics](https://mlsanity.com/paper/2503.23344/) (2025)
- [Gaze Heads: How VLMs Look at What They Describe](https://mlsanity.com/paper/2606.14703/) (2026)
- [A Comprehensive Gold Standard and Benchmark for Comics Text Detection and Recognition](https://mlsanity.com/paper/2212.14674/) (2022)
- [Evaluating Multimodal Large Language Models on Vertically Written Japanese Text](https://mlsanity.com/paper/2511.15059/) (2025)
- [Painting Style-Aware Manga Colorization Based on Generative Adversarial Networks](https://mlsanity.com/paper/2107.07943/) (2021)
- [AnySR: Realizing Image Super-Resolution as Any-Scale, Any-Resource](https://mlsanity.com/paper/2407.04241/) (2024)
- [Re:Verse -- Can Your VLM Read a Manga?](https://mlsanity.com/paper/2508.08508/) (2025)
- [MaRU: A Manga Retrieval and Understanding System Connecting Vision and Language](https://mlsanity.com/paper/2311.02083/) (2023)
- [Differentiable Search for Finding Optimal Quantization Strategy](https://mlsanity.com/paper/2404.08010/) (2024)
- [M2M-Gen: A Multimodal Framework for Automated Background Music Generation in Japanese Manga Using Large Language Models](https://mlsanity.com/paper/2410.09928/) (2024)
- [Visual representation of negation: Real world data analysis on comic image design](https://mlsanity.com/paper/2105.10131/) (2021)
- [Unlocking Comics: The AI4VA Dataset for Visual Understanding](https://mlsanity.com/paper/2410.20459/) (2024)
- [Context-Informed Machine Translation of Manga using Multimodal Large Language Models](https://mlsanity.com/paper/2411.02589/) (2024)
- [Co-Speech Gesture and Facial Expression Generation for Non-Photorealistic 3D Characters](https://mlsanity.com/paper/2506.16159/) (2025)
- [Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understanding](https://mlsanity.com/paper/2605.21182/) (2026)
- [MangaFlow: An End-to-End Agentic Framework for Controllable Story to Manga Generation](https://mlsanity.com/paper/2605.28173/) (2026)
- [Detection of Furigana Text in Images](https://mlsanity.com/paper/2207.03960/) (2022)

## Abstract (arXiv)

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.