Year: 2025
Type: article
Abstract: Historical documents from Late Antiquity to the early Middle Ages often suffer from degraded image quality due to aging, inadequate preservation, and environmental factors, presenting significant challenges for paleographical analysis. These documents contain crucial graphical symbols representing administrative, economic, and cultural information, which are time-consuming and error-prone to interpret manually. This research investigates image processing algorithms and deep learning models for enhancing these historical documents. Using image processing techniques, we improve symbol readability and visibility, while our deep learning approach aids in reconstructing degraded content and identifying patterns. This work contributes to improving the quality of historical document analysis, particularly for graphical symbol interpretation in paleographical studies.
Authors: Zahra Ziran, Massimo Mecella, Simone Marinai
Institutions: Sapienza University of Rome, University of Florence
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,990
OpenAlex ID: W4415442201