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Description
The transcription of historical administrative sources presents a distinctive set of challenges for automated text recognition: dense, formulaic layouts, highly abbreviated Latin and German terminology, and the cumulative degradation typical of large serial document corpora. This paper presents a high-precision OCR pipeline developed within the Unlocking the Schematismus project, designed to meet the specific demands of the Austrian Schematismus series, annual administrative directories spanning the Habsburg monarchy from the late eighteenth to the early twentieth century.
The pipeline integrates three complementary transcription models, each fine-tuned on annotated material from the corpus: a Kraken instance adapted to its typographic conventions, and two transformer-based vision models, Florence and TrOCR. Rather than selecting a single model in advance or averaging across outputs, the architecture adds a fourth component: a vision-language model used as a selection stage, which is shown the three candidate transcriptions together with the original image snippet and chooses the most plausible reading. Because this stage conditions on the image as well as on the textual candidates, it can resolve disagreements that neither confidence-based voting nor purely textual reranking would settle correctly.
The resulting pipeline achieves a character error rate of 0.2%, positioning it among the most precise HTR/OCR systems reported for comparable historical document types. Beyond the technical result, the paper reflects on the broader methodological implications: what does near-zero error mean in a corpus where orthographic inconsistency and abbreviation are themselves historically significant? And to what extent does the selection model encode implicit editorial decisions that warrant explicit philological scrutiny?
The Schematismus pipeline thus raises questions familiar from other corners of the digital humanities: the relationship between computational efficiency and scholarly responsibility, and the degree to which automated systems can, or should, replicate the interpretive judgments of the human editor.