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Ensuring an Error-Free Transcription on a Full Engineering Tags Dataset Through Unsupervised Post-OCR Methods

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Document Analysis and Recognition - ICDAR 2023 (ICDAR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14191))

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Abstract

The digital transformation of engineering documents is an ambitious research topic in the industrial world. The representation of component identifiers (tags), which are textual entities without a language model is one of the major challenges. Most of OCR use dictionary-based correction methods so they fail at recognizing hybrid entities composed by numerical and textual characters. This study aims to adapt OCR results on language-free strings with a specific semantics and requiring an efficient post-OCR correction with unsupervised approaches. We propose a two-step methodology to face the questions of post-OCR correction in engineering documents. The first step focuses on the alignment of OCR transcriptions producing a single prediction refined from all OCR predictions. The second step presents a combined incremental clustering & correction approach achieving a continuous correction of tags’ transcriptions relatively to their assigned cluster. For both steps, the dataset was produced from a set of 1,600 real technical documents and made available to the research community. When compared to the best state-of-art OCR, the post-OCR approach produced a gain of 9 % of WER.

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Notes

  1. 1.

    https://github.com/mathieuF789/dataset_tags.

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Authors and Affiliations

  1. Univ. Lyon, INSA Lyon, CNRS, UCBL, LIRIS, UMR5205, 69621, Villeurbanne, France

    Mathieu Francois & Véronique Eglin

  2. Orinox, Vaulx-en-Velin, France

    Mathieu Francois

Authors
  1. Mathieu Francois
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  2. Véronique Eglin
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Correspondence to Mathieu Francois or Véronique Eglin .

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Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. Adobe, College Park, MN, USA

    Rajiv Jain

  3. Osaka Metropolitan University, Osaka, Japan

    Koichi Kise

  4. Rochester Institute of Technology, Rochester, NY, USA

    Richard Zanibbi

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Francois, M., Eglin, V. (2023). Ensuring an Error-Free Transcription on a Full Engineering Tags Dataset Through Unsupervised Post-OCR Methods. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14191. Springer, Cham. https://doi.org/10.1007/978-3-031-41734-4_6

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  • DOI: https://doi.org/10.1007/978-3-031-41734-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41733-7

  • Online ISBN: 978-3-031-41734-4

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