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

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Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12824))

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Abstract

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.

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Notes

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Acknowledgment

This work has been supported by the Ministry of Culture Czech Republic in NAKI II project PERO (DG18P02OVV055).

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Authors and Affiliations

  1. Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic

    Jan Kohút & Michal Hradiš

Authors
  1. Jan Kohút
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  2. Michal Hradiš
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Correspondence to Jan Kohút .

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Editors and Affiliations

  1. Universitat Autònoma de Barcelona, Barcelona, Spain

    Josep Lladós

  2. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

  3. Kyushu University, Fukuoka-shi, Japan

    Seiichi Uchida

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Kohút, J., Hradiš, M. (2021). TS-Net: OCR Trained to Switch Between Text Transcription Styles. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12824. Springer, Cham. https://doi.org/10.1007/978-3-030-86337-1_32

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  • DOI: https://doi.org/10.1007/978-3-030-86337-1_32

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  • Print ISBN: 978-3-030-86336-4

  • Online ISBN: 978-3-030-86337-1

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