# Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understanding

arXiv: https://arxiv.org/abs/2605.21182 · HTML: https://mlsanity.com/paper/2605.21182/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 12 of 12)

- [OpenAI GPT-5 System Card](https://mlsanity.com/paper/2601.03267/) (2025)
- [Sketch-based Manga Retrieval using Manga109 Dataset](https://mlsanity.com/paper/1510.04389/) (2015)
- [Building a Manga Dataset "Manga109" with Annotations for Multimedia Applications](https://mlsanity.com/paper/2005.04425/) (2020)
- [Towards Fully Automated Manga Translation](https://mlsanity.com/paper/2012.14271/) (2020)
- [The Manga Whisperer: Automatically Generating Transcriptions for Comics](https://mlsanity.com/paper/2401.10224/) (2024)
- [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)
- [MangaUB: A Manga Understanding Benchmark for Large Multimodal Models](https://mlsanity.com/paper/2407.19034/) (2024)
- [One missing piece in Vision and Language: A Survey on Comics Understanding](https://mlsanity.com/paper/2409.09502/) (2024)
- [MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding](https://mlsanity.com/paper/2505.20298/) (2025)
- [From Panels to Prose: Generating Literary Narratives from Comics](https://mlsanity.com/paper/2503.23344/) (2025)

## Built on by (most cited first, 1 of 1)

- [Question-Guided Evidence Acquisition for Multimodal Visual Question Answering](https://mlsanity.com/paper/2608.19739/) (2026)

## Abstract (arXiv)

Manga is a culturally distinctive multimodal medium and one of the most influential forms of Japanese popular culture. As AI systems increasingly target manga understanding, OCR, and translation, Manga109 has become a foundational dataset for manga-related AI research. However, the current Manga109 dataset contains inaccurate transcriptions and coarse annotations, which do not align well with modern OCR and multimodal manga understanding tasks. In this work, we revisit the dialogue text annotations of Manga109 and identify five categories of annotation issues, including inaccurate transcriptions, missing text regions, overlapping dialogue and onomatopoeia, and under-segmented speech balloons. To address these issues, we combine OCR-based issue detection and manual revision to construct Manga109-v2026, revising approximately 29,000 dialogue annotations. Our revisions better align Manga109 with modern OCR and multimodal manga understanding systems while preserving expressive structures characteristic of manga.