MangaLens: A Multi-Model Tool for Structured Manga Parsing

Jiannan Mao; Chenchen Ding; Zhi Qu; Hideki Tanaka; Masao Utiyama

MangaLens: A Multi-Model Tool for Structured Manga Parsing

Jiannan Mao, Jin Tei, Zhi Qu, Hideki Tanaka, Masao Utiyama

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Abstract

Manga pages combine text, layout, characters, and narrative order, making them difficult to process as plain images or text alone. We present MangaLens, an integrated tool for structured manga parsing. MangaLens combines LLM-based reasoning with segmentation-based grounding to support manga parsing subtasks such as panel structuring, speaker linking, and related cross-page reasoning tasks. The system produces page-level and book-level JSON records and visualizes subtask outputs with aligned image evidence, enabling users to verify, correct, and reuse parsing artifacts. Evaluation results show that MangaLens produces reliable structured parsing outputs across major subtasks. The tool is released at https://github.com/nict-astrec-att/MangaLens

Anthology ID:: 2026.emnlp-demos.12
Volume:: Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Sweta Agrawal, Alberto Abad
Venue:: EMNLP
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 128–137
Language:
URL:: https://aclanthology.org/2026.emnlp-demos.12/
DOI:
Bibkey:
Cite (ACL):: Jiannan Mao, Jin Tei, Zhi Qu, Hideki Tanaka, and Masao Utiyama. 2026. MangaLens: A Multi-Model Tool for Structured Manga Parsing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 128–137, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: MangaLens: A Multi-Model Tool for Structured Manga Parsing (Mao et al., EMNLP 2026)
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PDF:: https://aclanthology.org/2026.emnlp-demos.12.pdf

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@inproceedings{mao-etal-2026-mangalens,
    title = "{M}anga{L}ens: A Multi-Model Tool for Structured Manga Parsing",
    author = "Mao, Jiannan  and
      Tei, Jin  and
      Qu, Zhi  and
      Tanaka, Hideki  and
      Utiyama, Masao",
    editor = "Agrawal, Sweta  and
      Abad, Alberto",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.emnlp-demos.12/",
    pages = "128--137",
    ISBN = "979-8-89176-461-3",
    abstract = "Manga pages combine text, layout, characters, and narrative order, making them difficult to process as plain images or text alone. We present MangaLens, an integrated tool for structured manga parsing. MangaLens combines LLM-based reasoning with segmentation-based grounding to support manga parsing subtasks such as panel structuring, speaker linking, and related cross-page reasoning tasks. The system produces page-level and book-level JSON records and visualizes subtask outputs with aligned image evidence, enabling users to verify, correct, and reuse parsing artifacts. Evaluation results show that MangaLens produces reliable structured parsing outputs across major subtasks. The tool is released at \url{https://github.com/nict-astrec-att/MangaLens}"
}

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    <abstract>Manga pages combine text, layout, characters, and narrative order, making them difficult to process as plain images or text alone. We present MangaLens, an integrated tool for structured manga parsing. MangaLens combines LLM-based reasoning with segmentation-based grounding to support manga parsing subtasks such as panel structuring, speaker linking, and related cross-page reasoning tasks. The system produces page-level and book-level JSON records and visualizes subtask outputs with aligned image evidence, enabling users to verify, correct, and reuse parsing artifacts. Evaluation results show that MangaLens produces reliable structured parsing outputs across major subtasks. The tool is released at https://github.com/nict-astrec-att/MangaLens</abstract>
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%0 Conference Proceedings
%T MangaLens: A Multi-Model Tool for Structured Manga Parsing
%A Mao, Jiannan
%A Tei, Jin
%A Qu, Zhi
%A Tanaka, Hideki
%A Utiyama, Masao
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F mao-etal-2026-mangalens
%X Manga pages combine text, layout, characters, and narrative order, making them difficult to process as plain images or text alone. We present MangaLens, an integrated tool for structured manga parsing. MangaLens combines LLM-based reasoning with segmentation-based grounding to support manga parsing subtasks such as panel structuring, speaker linking, and related cross-page reasoning tasks. The system produces page-level and book-level JSON records and visualizes subtask outputs with aligned image evidence, enabling users to verify, correct, and reuse parsing artifacts. Evaluation results show that MangaLens produces reliable structured parsing outputs across major subtasks. The tool is released at https://github.com/nict-astrec-att/MangaLens
%U https://aclanthology.org/2026.emnlp-demos.12/
%P 128-137

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Markdown (Informal)

[MangaLens: A Multi-Model Tool for Structured Manga Parsing](https://aclanthology.org/2026.emnlp-demos.12/) (Mao et al., EMNLP 2026)

MangaLens: A Multi-Model Tool for Structured Manga Parsing (Mao et al., EMNLP 2026)

ACL

Jiannan Mao, Jin Tei, Zhi Qu, Hideki Tanaka, and Masao Utiyama. 2026. MangaLens: A Multi-Model Tool for Structured Manga Parsing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 128–137, Budapest, Hungary. Association for Computational Linguistics.