Speaker
Description
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.