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Layout Detection and Table Recognition – Recent Challenges in Digitizing Historical Documents and Handwritten Tabular Data

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Digital Libraries for Open Knowledge (TPDL 2020)

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Abstract

In this paper, we discuss the computer-aided processing of handwritten tabular records of historical weather data. The observationes meteorologicae, which are housed by the Regensburg University Library, are one of the oldest collections of weather data in Europe. Starting in 1771, meteorological data was consistently documented in a standardized form over almost 60 years by several writers. The tabular structure, as well as the unconstrained textual layout of comments and the use of historical characters, propose various challenges in layout and text recognition. We present a customized strategy to digitize tabular and handwritten data by combining various state-of-the-art methods for OCR processing to fit the collection. Since the recognition of historical documents still poses major challenges, we provide lessons learned from experimental testing during the first project stages. Our results show that deep learning methods can be used for text recognition and layout detection. However, they are less efficient for the recognition of tabular structures. Furthermore, a tailored approach had to be developed for the historical meteorological characters during the manual creation of ground truth data. The customized system achieved an accuracy rate of 82% for the text recognition of the heterogeneous handwriting and 87% accuracy for layout recognition of the tables.

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Author information

Authors and Affiliations

  1. University Library of Regensburg, 93053, Regensburg, Germany

    Constantin Lehenmeier

  2. Computational Humanities, Leipzig University, 04109, Leipzig, Germany

    Manuel Burghardt

  3. University of Regensburg, 93053, Regensburg, Germany

    Bernadette Mischka

Authors
  1. Constantin Lehenmeier
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  2. Manuel Burghardt
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  3. Bernadette Mischka
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Corresponding author

Correspondence to Constantin Lehenmeier .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, The Open University, Milton Keynes, UK

    Mark Hall

  2. Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia

    Tanja Merčun

  3. University Library J. C. Senckenberg, Goethe University Frankfurt, Frankfurt am Main, Germany

    Thomas Risse

  4. Université Claude Bernard Lyon 1, Villeurbanne, France

    Fabien Duchateau

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Lehenmeier, C., Burghardt, M., Mischka, B. (2020). Layout Detection and Table Recognition – Recent Challenges in Digitizing Historical Documents and Handwritten Tabular Data. In: Hall, M., Merčun, T., Risse, T., Duchateau, F. (eds) Digital Libraries for Open Knowledge. TPDL 2020. Lecture Notes in Computer Science(), vol 12246. Springer, Cham. https://doi.org/10.1007/978-3-030-54956-5_17

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  • DOI: https://doi.org/10.1007/978-3-030-54956-5_17

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