A deep learning experiment for semantic segmentation of overlapping characters in palimpsests
This addresses the challenge of deciphering historical manuscripts with overlapping writings for historians and archaeologists, though it appears incremental as a proof of concept.
The researchers tackled the problem of identifying individual letters in overlapping characters in palimpsests using deep learning-based semantic segmentation, achieving a proof of concept on the Ars Grammatica by Prisciano case study.
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.