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 embeddings. We also show how TSB can efficiently adapt to transcription styles of new documents from transcriptions of only a few text lines.
CITATION STYLE
Kohút, J., & Hradiš, M. (2021). TS-Net: OCR Trained to Switch Between Text Transcription Styles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12824 LNCS, pp. 478–493). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-86337-1_32
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