Computational Notebook - Methods applied to the hoard of Le Câtillon II in the project ClaReNet Computational Notebook - Methods applied to the hoard of Le Câtillon II in the project ClaReNet

ClaReNet is a joint project of the Römisch-Germanische Kommission (German Archaeological Institute) and the Big Data Lab (Goethe University Frankfurt), funded by the German Federal Ministry of Education and Research (BMBF). It tests the possibilities and limits of new digital methods of classification and representation. This supplement provides a snapshot of the methods used in the project, which will be published in the paper "Supporting the analysis of a large coin hoard with AI-based methods'' submitted to CAA 2023, and is based on the Github repository "https://github.com/Frankfurt-BigDataLab/2023_CAA_ClaReNet". The core of the analysis were digital images of around 70,000 coins discovered in a coin hoard at Le Câtillon II on the island of Jersey (UK), and the methods used fall into four parts: 1. Object detection: cropping the image to the coin and reducing bias; 2. Unsupervised learning: observing the way the coins are grouped independently of domain information; 3. Supervised learning: combining the results of unsupervised learning and domain information; 4. Image matching: detecting similar dies. The snapshot is used to show and reproduce the analysis at the time of the paper. Further information and sources can be found on the official Github repository.