David Fleischhacker | Searcharxiv

arXiv2026

Design and Operation of a Federated GPU Cluster for Digital Humanities within DHinfra.at

We describe the design, implementation, and operation of a small federated GPU cluster built for Digital Humanities (DH) research within the Austrian DHinfra.at project. The system spans two university sites, brokers logins from a national identity federation, and exposes compute through three interfaces: interactive notebooks, SSH, and an OpenAI-compatible inference API. The core of the platform is a control plane that maps federated identity, per-project quotas, and a model catalogue onto self-service interfaces. We document this control plane in detail, including the request-and-approval queue that mediates every privileged cluster effect and the two-tier model-serving setup, which combines always-on services with on-demand model swapping and lets researchers consume large language models over HTTPS without holding a cluster account. We report the constraints that shaped the build: a fixed power budget, EU-wide procurement, a team of about 1.5 full-time equivalents, and the relationship between prioritized access and utilization. We also record the main build decisions against the alternatives we researched and did not adopt. The platform is released under the Apache License 2.0. We close with a playbook for teams building a domain-specific GPU cluster on a limited budget. This is a living technical report, versioned and updated as the platform evolves.

cs.DC↗

arXiv2024

Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach

This paper addresses a major challenge to historical research on the 19th century. Large quantities of sources have become digitally available for the first time, while extraction techniques are lagging behind. Therefore, we researched machine learning (ML) models to recognise and extract complex data structures in a high-value historical primary source, the Schematismus. It records every single person in the Habsburg civil service above a certain hierarchical level between 1702 and 1918 and documents the genesis of the central administration over two centuries. Its complex and intricate structure as well as its enormous size have so far made any more comprehensive analysis of the administrative and social structure of the later Habsburg Empire on the basis of this source impossible. We pursued two central objectives: Primarily, the improvement of the OCR quality, for which we considered an improved structure recognition to be essential; in the further course, it turned out that this also made the extraction of the data structure possible. We chose Faster R-CNN as base for the ML architecture for structure recognition. In order to obtain the required amount of training data quickly and economically, we synthesised Hof- und Staatsschematismus-style data, which we used to train our model. The model was then fine-tuned with a smaller set of manually annotated historical source data. We then used Tesseract-OCR, which was further optimised for the style of our documents, to complete the combined structure extraction and OCR process. Results show a significant decrease in the two standard parameters of OCR-performance, WER and CER (where lower values are better). Combined structure detection and fine-tuned OCR improved CER and WER values by remarkable 71.98 percent (CER) respectively 52.49 percent (WER).

cs.CV↗