  Users of OCR systems, from different institutions and scientific disciplines,
prefer and produce different transcription styles. This presents a problem for
training of consistent text recognition neural networks on real-world data. We
propose to extend existing text recognition networks with a Transcription Style
Block (TSB) which can learn from data to switch between multiple transcription
styles without any explicit knowledge of transcription rules. TSB is an
adaptive instance normalization conditioned by identifiers representing
consistently transcribed documents (e.g. single document, documents by a single
transcriber, or an institution). We show that TSB is able to learn completely
different transcription styles in controlled experiments on artificial data, it
improves text recognition accuracy on large-scale real-world data, and it
learns semantically meaningful transcription style embedding. We also show how
TSB can efficiently adapt to transcription styles of new documents from
transcriptions of only a few text lines.
