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Description
Automatic Text Recognition has become the most widely adopted application of machine learning in the humanities, yet projects often encounter challenges when integrating existing models into their workflow. This paper dicusses the source of these challenges as a mismatch between requirement profiles - a concept broader than transcription guidelines, constituted by three dimensions: disciplinary standards, downstream implementation requirements , and the „Erschließungsmodus“, the degree of acceptable intervention by a model. Since ATR models are data-deterministic, current Research hast to choose between two strategies: heterogeneous „super models“ offering coverage but also unpredictable output, or convention-consistent models that are predictable but rigid, costly to produce, and move the problem into downstream postprocessing.
The paper proposes a third option: encoding the requirement profile in the model itself. Extending the language-token mechanism of Benjamin Kiessling's party architecture with two conditioning tokens (corpus: transcription convention and character inventory, and mode: abbreviated / expanded), and replicating the approach in a CNN based model, experiments on data from Burchards Dekret Digital show that transcription behaviour can be steered reliably: expected abbreviation markers appear in 83–86% of cases in abbreviated mode and are suppressed in 99–100% of cases in expanded mode, with steerability persisting when the training pool is enlarged by CATMuS and Tridis material and when BDD's token is forced onto unseen corpora.
Building on this proof of concept, the paper argues for a shared, machine-readable taxonomy of requirement profiles that would standardise the description of transcription practice rather than the practice itself.