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Users of OCR systems, from different institutions and scientific disciplines,prefer and produce different transcription styles. This presents a problem fortraining of consistent text recognition neural networks on real-world data. Wepropose to extend existing text recognition networks with a Transcription StyleBlock (TSB) which can learn from data to switch between multiple transcriptionstyles without any explicit knowledge of transcription rules. TSB is anadaptive instance normalization conditioned by identifiers representingconsistently transcribed documents (e.g. single document, documents by a singletranscriber, or an institution). We show that TSB is able to learn completelydifferent transcription styles in controlled experiments on artificial data, itimproves text recognition accuracy on large-scale real-world data, and itlearns semantically meaningful transcription style embedding. We also show howTSB can efficiently adapt to transcription styles of new documents fromtranscriptions of only a few text lines.

TS-Net: OCR Trained to Switch Between Text Transcription Styles