A stemma (pl. stemmata) is a genealogical trees of manuscripts used by philologists and editors to represent the history of textual traditions of manuscript texts since the early XIXth century. The emergence of the complex system approach to cultural dynamics as well as long-lasting methodological controversies in philology have driven the demand for a comprehensive, cross-corpus database of stemmata. The collection of such a database remains a tedious task due the fact that published stemmata are scattered among a vast body of scientific publications and critical editions, with widely heterogeneous representation conventions.

The object of this work is to present an automated detection and transcription pipeline of stemmata designed to extract relevant informations from large libraries of digitized publications, leveraging recent development in computer vision and vision-language models. The output of this pipeline is a a structured, XML-based representation of stemmata enriched with metadata on corresponding textual traditions and witnesses, following the guidelines of the OpenStemmata project.

We present a detailed outline of our protocol, with quantitative evaluations of the performances of the subtasks of this pipeline