Virtual restoration and content analysis of ancient degraded manuscripts

  • Anna Tonazzini ISTI-CNR
  • Pasquale Savino ISTI-CNR
  • Emanuele Salerno ISTI-CNR
  • Muhammad Hanif
  • Franca Debole

Abstract

In recent years, extensive campaigns of digitization of the documental heritage conserved in libraries and archives have been performed, with the primary goal to ensure thepreservation and fruition of this important part of the human cultural and historical patrimony. Besides protecting conservation, the availability of high quality digital copies has increasingly stimulated the use of image processing techniques, to perform a number ofoperations on documents and manuscripts, without harming the often precious and fragile originals. Among those, virtual restoration tasks are crucial, as they facilitate the traditional work of philologists and paleographers, and constitute a first step towards an automatic analysis of the written contents. Here we report our experience in this field, referring, as a case study, to the problem of removing one of the mostfrequent and impairing degradations affecting ancient manuscripts, i.e., the bleed-through distortion. We show that techniques of blind source separation are versatile tools to either cancel these unwanted interferences or isolate specific features for content analysis goals. Specialized algorithms, based on recto-verso models and sparse image representation, are then shown to be able to perform a fine and selective removal of the degradation, while preserving the original appearance of the manuscript.
Published
Sep 14, 2019
How to Cite
TONAZZINI, Anna et al. Virtual restoration and content analysis of ancient degraded manuscripts. International Journal of Information Science and Technology, [S.l.], v. 3, n. 5, p. 16 - 25, sep. 2019. ISSN 2550-5114. Available at: <https://innove.org/ijist/index.php/ijist/article/view/133>. Date accessed: 16 apr. 2024. doi: http://dx.doi.org/10.57675/IMIST.PRSM/ijist-v3i5.133.
Section
Special Issue : Machine Learning and Natural Language Processing