Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understanding

TL;DR

This paper revisits and revises the Manga109 dataset's dialogue annotations to improve its utility for modern OCR and multimodal manga understanding systems, addressing previous annotation issues.

Contribution

The authors identify annotation issues in Manga109 and manually revise approximately 29,000 dialogue annotations to enhance dataset quality.

Findings

Revised annotations improve alignment with modern OCR systems.

Addressed issues like transcription errors and overlapping text.

Enhanced dataset supports advanced manga understanding tasks.

Abstract

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 transcription errors 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 transcription errors, 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.