A deep learning experiment for semantic segmentation of overlapping
  characters in palimpsests
Michela Perino Michele Ginolfi Anna Candida Felici Michela Rosellini
Palimpsests refer to historical manuscripts where erased writings have been
partially covered by the superimposition of a second writing. By employing
imaging techniques, e.g., multispectral imaging, it becomes possible to
identify features that are imperceptible to the naked eye, including faded and
erased inks. When dealing with overlapping inks, Artificial Intelligence
techniques can be utilized to disentangle complex nodes of overlapping letters.
In this work, we propose deep learning-based semantic segmentation as a method
for identifying and segmenting individual letters in overlapping characters.
The experiment was conceived as a proof of concept, focusing on the palimpsests
of the Ars Grammatica by Prisciano as a case study. Furthermore, caveats and
prospects of our approach combined with multispectral imaging are also
discussed.