How the PAGE Framework Automates AI Comic Analysis

How Does AI Analyze Visual Patterns in Graphic Narratives?

The PAGE framework provides a method for using artificial intelligence to analyze large collections of American comics and Japanese manga. Researchers developed this tool to quantify stylistic differences and visual patterns across thousands of pages. It functions by automating the extraction and classification of visual elements within these graphic narratives. As a preprint released on October 9, 2026, this work has not yet undergone peer review. Readers should treat these findings as preliminary findings rather than settled science.

Using Machine Learning to Quantify Stylistic Trends in Comics

The research team applied this framework to three specific studies comparing visual techniques in American and Japanese works. They used machine learning models to identify recurring features like panel layouts, character placement, and dialogue density. By processing these images at scale, the researchers identified distinct structural trends that differ between the two mediums. It is important to remember that correlation is not causation; this tool finds patterns but does not explain the cultural or editorial intent behind them. Because this is a computational approach, the accuracy of the results depends on the quality of the training data. Future versions of the framework might expand to include color analysis or more complex narrative tracking. For now, the framework is a proof of concept for scholars interested in digital humanities and graphic storytelling.