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Mathematical Models and Neural Networks for the Description and the Correction of Typical Distortions of Historical Manuscripts

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14108))

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Abstract

Historical manuscripts are very often degraded by the seeping or transparency of the ink from the page opposite side. Suppressing the interfering text can be of great aid to philologists and paleographers who aim at interpreting the primary text, and nowadays also for the automatic analysis of the text. We formerly proposed a data model, which approximately describes this damage, to generate an artificial training set able to teach a shallow neural network how to classify pixels in clean or corrupted. This NN has proved to be effective in classifying manuscripts where the degradation can be also widely variable. In this paper, we modify the architecture of the NN to better account for ink saturation in text overlay areas, by including a specific class for these pixels. From the experiments, the improvement of the classification and then the restoration is significant.

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Authors and Affiliations

  1. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Via G. Moruzzi 1, 56124, Pisa, Italy

    Pasquale Savino & Anna Tonazzini

Authors
  1. Pasquale Savino
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  2. Anna Tonazzini
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Corresponding author

Correspondence to Anna Tonazzini .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

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Savino, P., Tonazzini, A. (2023). Mathematical Models and Neural Networks for the Description and the Correction of Typical Distortions of Historical Manuscripts. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14108. Springer, Cham. https://doi.org/10.1007/978-3-031-37117-2_37

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  • DOI: https://doi.org/10.1007/978-3-031-37117-2_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37116-5

  • Online ISBN: 978-3-031-37117-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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