# Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga

arXiv: https://arxiv.org/abs/2610.13840 · HTML: https://mlsanity.com/paper/2610.13840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 70 references that are in the map.
- First posted on arXiv 9 Oct 2026.

## Builds on (most cited first, 30 of 33)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Sketch-based Manga Retrieval using Manga109 Dataset](https://mlsanity.com/paper/1510.04389/) (2015)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models](https://mlsanity.com/paper/2302.04222/) (2023)
- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning](https://mlsanity.com/paper/2304.06588/) (2023)
- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)
- [Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models](https://mlsanity.com/paper/2310.13828/) (2023)
- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives](https://mlsanity.com/paper/1611.05118/) (2016)
- [GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models](https://mlsanity.com/paper/2310.00737/) (2023)
- [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)
- [Comicolorization: Semi-Automatic Manga Colorization](https://mlsanity.com/paper/1706.06759/) (2017)
- [DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation](https://mlsanity.com/paper/2412.07589/) (2024)
- [CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding](https://mlsanity.com/paper/2407.03550/) (2024)
- [The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America](https://mlsanity.com/paper/2005.01583/) (2020)
- [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)
- [A survey of comics research in computer science](https://mlsanity.com/paper/1804.05490/) (2018)
- [Compression ensembles quantify aesthetic complexity and the evolution of visual art](https://mlsanity.com/paper/2205.10271/) (2022)
- [Zero-Shot Character Identification and Speaker Prediction in Comics via Iterative Multimodal Fusion](https://mlsanity.com/paper/2404.13993/) (2024)
- [MangaUB: A Manga Understanding Benchmark for Large Multimodal Models](https://mlsanity.com/paper/2407.19034/) (2024)
- [Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits](https://mlsanity.com/paper/2605.18890/) (2026)
- [MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding](https://mlsanity.com/paper/2505.20298/) (2025)
- [Synthetic Politics: Prevalence, Spreaders, and Emotional Reception of AI-Generated Political Images on X](https://mlsanity.com/paper/2502.11248/) (2025)

## Abstract (arXiv)

Comics are one of the great visual records of the twentieth century: hundreds of thousands of pages in which a mass readership saw its leaders, its wars and its heroes. Most of this record has never been studied as a whole; comics scholarship proceeds one book at a time. We argue that artificial intelligence, including generative models, can change this to the benefit of the medium itself. We propose PAGE, a framework that makes artificial intelligence an instrument for comics research. It parses every scan into panels, text and a bibliographic record; annotates each page with a vision-language model that follows a written codebook, as a human coder would; grounds samples of these readings in human annotation; and explains change across an archive, with estimates controlled for publisher and genre. We demonstrate the framework on 3,929 American comic books (1938--1956) and 87 volumes of Japanese manga, in three studies, each resting on a different capability. The first pairs text search with visual reading to recover how a mass medium portrayed political power. The second measures the page's visual form across an industry and recovers the hand of individual artists. The third follows who speaks across two comics traditions, checked against human annotation. Beyond research, the same instruments can make archives searchable by what is drawn, open comics to blind and low-vision readers, and help preserve fragile collections and credit uncredited artists. We outline a research agenda in which artificial intelligence strengthens the study, preservation and reach of the medium.