Supporting a Celtic coin die study with image matching and hierarchical clustering for creating a social networks analysis

Studying coin dies, particularly Celtic ones, is a challenging and time-consuming task for numismatists. To support them in this task, we implemented a coin studies pipeline in ClaReNet, which we have further improved in our current DeReNum project. The aim is to maintain human involvement and oversight, which is particularly important given the challenges posed by Celtic coins for die studies, as detailed in the paper. Notably, our focus is on silver coins, which are often in worse condition than gold coins. Extremely poorly preserved coins can be eliminated by a pre-sorting step supported by the Deepcluster method. The numismatist will also be involved in this step and will have the final decision. Pre-sorting with Deepcluster is particularly useful when the number of coins is high or the state of preservation varies greatly. As a result of the pipeline, which will be explained in detail in this paper, the user will receive a dendrogram ordering the coin images (one image per coin side) and proposing clusters of coins likely to have been produced from the same die. Using the open-source tool Orange Data Mining, we also propose a way to visualise the dendrogram and provide a working environment.Within the pipeline, we are currently comparing two image-matching algorithms: ORB and DISK. Based on tests with a dataset using a ground truth from the ClaReNet project, DISK, which is based on convolutional neural networks, performed better than ORB. However, another test showed that ORB can handle coins with different orientations more effectively.In this paper, we will present examples of new dies identified using our pipeline. However, we are still in the early stages of the project and are currently collecting material for analysis.