Manga, or Japanese comics, is a richly multimodal narrative form that blends images and text in complex ways. Teaching large multimodal models (LMMs) to understand such narratives at a human-like level could help manga creators reflect on and refine their stories. To this end, we introduce two benchmarks for multimodal manga understanding: MangaOCR, which targets in-page text recognition, and MangaVQA, a novel benchmark designed to evaluate contextual understanding through visual question answering. MangaVQA consists of 526 high-quality, manually constructed question-answer pairs, enabling reliable evaluation across diverse narrative and visual scenarios. Building on these benchmarks, we develop MangaLMM, a manga-specialized model finetuned from the open-source LMM Qwen2.5-VL to jointly handle both tasks. Through extensive experiments, including comparisons with proprietary models such as GPT-4o and Gemini 2.5, we assess how well LMMs understand manga. Our benchmark and model provide a comprehensive foundation for evaluating and advancing LMMs in the richly narrative domain of manga.
@article{arxiv.2505.20298,
title = {MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding},
author = {Jeonghun Baek and Kazuki Egashira and Shota Onohara and Atsuyuki Miyai and Yuki Imajuku and Hikaru Ikuta and Kiyoharu Aizawa},
journal= {arXiv preprint arXiv:2505.20298},
year = {2026}
}