Computational methods have transformed many areas of historical research, yet paleography has seen comparatively few algorithms and tools that can operate at scale while remaining adaptable to scholarly practices across different scripts. This is not due to a lack of interest in digital methods. The growth of digital repositories, annotation platforms such as DigiPal, and numerous prototypes using modern machine learning all demonstrate the field’s openness to computational approaches. What remains lacking are methods that can support paleographic investigation without narrowing it to a predefined set of categories or research questions.

This presentation explores automatic text recognition (ATR) as one such method. Rather than treating ATR only as a means of producing transcriptions, it considers how recognition models can serve as instruments for paleographic research. Their combination of adaptability, high throughput, and introspectability makes it possible to examine large bodies of material while retaining access to the individual written forms on which an analysis is based.

Placed near the bottom of the ladder of abstraction, this approach offers a way to formulate research questions and validate observations without presupposing an established paleographic framework. It may therefore be particularly valuable for the study of non-Western and minority writing traditions, for which inherited classifications may be incomplete, inappropriate, or altogether absent.