Next-generation Handwritten Text Recognition, Agentic Coding, and Digital Sovereignty

Coding Agents such as Anthropic’s Claude Code or OpenAI’s Codex have significantly expanded the ways humanists can address the retrieval, analysis and presentation of cultural heritage. At the same time, the latest iterations of the LLMs they are based on exhibit astonishing capabilities in Handwritten Text Recognition (HTR), often rivalling dedicated HTR applications and models. However, these benefits come at the cost of well-known drawbacks of commercial LLMs (problematic business model of the large US providers, carbon footprint, data centers, issues of data protection, intellectual property, and dependence on external services, among others). Additionally, due to their biases towards modern standard languages, LLMs used for HTR purposes often silently normalize or modernize their transcriptions, thereby distorting the results and making certain investigations impossible.

In my presentation, I provide an example of how to navigate this complex landscape. I present the web version of the multi-source HTR tool Polyscriptor and discuss how and under what conditions to replace commercial agentic models with open-weight models, thus fostering digital sovereignty. The archival material I will provide as an example is a corpus of postcards written by Soviet Ukrainian people deported to Nazi Germany for forced labor. Many of the postcards are written in complex layouts in a mixture of Russian and Ukrainian, requiring specific solutions for HTR.

This presentation outlines a pragmatic approach to balancing the capabilities of commercial models with the goal of greater digital sovereignty.