# A deep learning experiment for semantic segmentation of overlapping characters in palimpsests

Source: [A deep learning experiment for semantic segmentation of overlapping characters in palimpsests](<https://www.sota2.com/research/paper/68fa71141e209131cea4f299>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:02.230Z

Authors: Michela Perino, Michele Ginolfi, Anna Candida Felici, Michela Rosellini

Published: 2023-11-02T10:25:47.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2311.01130>)

[PDF](<https://arxiv.org/pdf/2311.01130.pdf>)