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ReadOCR: A Novel Dataset and Readability Assessment of OCRed Texts

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Document Analysis Systems (DAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13237))

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Abstract

Results of digitisation projects sometimes suffer from the limitations of optical character recognition software which is mainly designed for modern texts. Prior work has examined the impact of OCR errors on information retrieval (IR) and downstream natural language processing (NLP) tasks. However, questions remain open regarding the actual readability of the OCRed text to the end users, especially, considering that traditional OCR quality metrics consider only syntactic or surface features and are quite limited. This paper proposes a novel dataset and conducts a pilot study to investigate these questions.

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Notes

  1. 1.

    https://tinyurl.com/ReadOCR.

  2. 2.

    https://www.kaggle.com/c/commonlitreadabilityprize/data.

  3. 3.

    https://en.wikipedia.org.

  4. 4.

    https://www.africanstorybook.org.

  5. 5.

    https://www.commonlit.org.

  6. 6.

    The three annotators are sophomores, two of them are law students, and one is an information technology student.

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Acknowledgements

This work has been supported by the “ANNA” and “Au-delà des Pyrénées” projects funded by the Nouvelle-Aquitaine region.

Author information

Authors and Affiliations

  1. Posts and Telecommunications Institute of Technology, Ho Chi Minh, Vietnam

    Hai Thi Tuyet Nguyen

  2. Department of Computer Science, University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

  3. L3i, La Rochelle University, La Rochelle, France

    Mickaël Coustaty & Antoine Doucet

Authors
  1. Hai Thi Tuyet Nguyen
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  2. Adam Jatowt
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  3. Mickaël Coustaty
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  4. Antoine Doucet
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Corresponding author

Correspondence to Hai Thi Tuyet Nguyen .

Editor information

Editors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Seiichi Uchida

  2. Boise State University, BOISE, ID, USA

    Elisa Barney

  3. LIRIS UMR CNRS, Villeurbanne, France

    Véronique Eglin

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Nguyen, H.T.T., Jatowt, A., Coustaty, M., Doucet, A. (2022). ReadOCR: A Novel Dataset and Readability Assessment of OCRed Texts. In: Uchida, S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. Lecture Notes in Computer Science, vol 13237. Springer, Cham. https://doi.org/10.1007/978-3-031-06555-2_32

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  • DOI: https://doi.org/10.1007/978-3-031-06555-2_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06554-5

  • Online ISBN: 978-3-031-06555-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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