Ancient documents are important historical sources that are often found in a fragmented condition due to their conservation status. In this study, we examined fragments of paper found in 1996 during excavation of the Santi Quattro Coronati complex, in Rome. The archaeological site where the fragments were found is situated on the first floor of the tower within the complex. This location was used as a disposal pit approximately between the 15th and 16th centuries. The fragments exhibit text discoloration, hindering automatic recognition and human readability. To reveal the faded text, the fragments have been digitalized, converted into a perceptually uniform color space and the contrast has been enhanced. The photometric characteristics of the input and enhanced images have been statistically characterized, and the contrast enhancement assessed by a state-of-the-art metric. The statistical analysis of the text colour coordinates was carried out to develop supervised and unsupervised image segmentation, isolating the text. The results of the method show that it effectively identifies text regions within images, improving readability, even for faded text. It can be integrated into deep learning-based character recognition systems, facilitating the automatic analysis of historical handwritten documents.