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Estimating Human Legibility in Historic Manuscript Images - A Baseline

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Book cover Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12823))

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Abstract

For accessing degraded historic text sources, humanist research increasingly relies on image processing for the digital restoration of written artifacts. A problem of these restoration approaches is the lack of a generally applicable objective method to assess the results. In this work we motivate the need for a quality metric for historic manuscript images, that explicitly targets human legibility. Reviewing previous attempts to evaluate the quality of manuscript images or the success of text restoration methods, we can not find a satisfying solution: either the approaches have a limited applicability, or they are insufficiently validated with respect to human perception. In order to establish a baseline for further research in this area, we test several candidates for human legibility estimators, while proposing an evaluation framework based on a recently published dataset of expert-rated historic manuscript images.

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Authors and Affiliations

  1. Institute of Visual Computing and Human-Centered Technology, TU Wien, 1040, Vienna, Austria

    Simon Brenner, Lukas Schügerl & Robert Sablatnig

Authors
  1. Simon Brenner
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  2. Lukas Schügerl
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  3. Robert Sablatnig
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Correspondence to Simon Brenner .

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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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Brenner, S., Schügerl, L., Sablatnig, R. (2021). Estimating Human Legibility in Historic Manuscript Images - A Baseline. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12823. Springer, Cham. https://doi.org/10.1007/978-3-030-86334-0_32

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  • DOI: https://doi.org/10.1007/978-3-030-86334-0_32

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  • Online ISBN: 978-3-030-86334-0

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