The feasibility of machine-learning based workflows for editing serial historical sources : First results of the Schenkenschans customs registers project

Forthcoming in: Machine learning and data mining for digital scholarly editions / Ulrike Henny-Krahmer (ed.): Norderstedt 2023

This paper discusses preliminary results of a project that aims to facilitate the study of logistics patterns in German-Dutch transport and trade on the Rhine in the early modern period by means of a digital edition of the customs registers of the Schenkenschans (SSZ). Based on an ongoing pilot study with a sample of the SSZ registers, the paper discusses the use of machine-learning based tools for HTR as starting point for creating a digital scholarly edition of serial historical sources. Building on a conceptualizing of the customs registrations in the SSZ as economic movement data, the paper briefly outlines how the scholarly edition supports data mining and documentation. Based on rigorous timing of the different steps in the proposed workflow, the pilot study assesses the feasibility of ML-based tools for HTR as a first step in the production of a digital scholarly edition of the SSZ for data mining and documentary purposes.