Improving the Identification of Layers in 3D Images of Ancient Papyrus using Artificial Neural Networks

Nicolas Klenert, Finn Schwoerer, Noushin Hajarolasvadi, Siloé Bournez, Tobias Arlt, Heinz-Eberhard Mahnke, Verena Lepper, Daniel Baum; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1294-1302

The process of digitally unfolding ancient documents such as folded papyrus packages from 3D image data aims to be a non-invasive means to make previously hidden writing visible without risking to damage the precious documents. One of the main tasks necessary to digitally unfold a document is the geometric reconstruction of the writing substrate which is a prerequisite for its subsequent unfolding. All current reconstruction methods require the existence of an interspace between different layers of the document to ensure a correct topology. Layers that appear merged together in the 3D image often result in wrong connections between layers and thus also in a wrong topology of the reconstructed geometry which hinders the successful unfolding. Here we propose to use a neural network to facilitate the discrimination of the layers. Using papyrus documents as an example of a particularly difficult writing material we show that this significantly reduces the number of wrong connections and improves the overall identification of the layers. This in turn enables fully automatic digital unfolding of large areas of highly complex papyrus packages. Utilizing explainable AI (XAI) further allows us to explore the results of the applied neural network.