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

The non-invasive digital unfolding of ancient documents, such as folded papyrus packages, from 3D image data aims to reveal previously hidden writing without risking to damage the precious documents. One of the main tasks in this process is the geometric reconstruction of the writing sub-strate, 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 to-gether 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 our approach 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.