AnCoins: Image-Based Automated Identification of Ancient Coins Through Transfer Learning Approaches

The identification of ancient coins is a time consuming and complex task with huge experience demands. The analysis of numismatic evidence through patterns detection executed by Machine Learning methods has started to be recognized as approaches that can provide archaeologists with a wide range of tools, which, especially in the fields of numismatics, can be used to ascertain distribution, continuity, change in engraving style and imitation. In this paper we introduce what we call the Ancient Coins (AnCoins-12) dataset. Α set of images composed of 12 different classes of Greek ancient coins from the area of ancient Thrace, aiming for the automatic identification of their issuing authority. In this context we describe the methodology of data acquisition and dataset organization emphasizing the small number of images available in this field. In addition to that we apply deep learning approaches based on popular CNN architectures to classify the images of the new introduced dataset. Pre-trained CNNs, through transfer learning approaches, achieved a top-1 validation accuracy of 98.32% and top-5 validation accuracy of 99.99%. For a better diffusion of the results in the archaeological community, we introduce a responsive web-based application with an extension asset focusing in the identification of common characteristics in different coin types. We conclude the paper, by stressing some of the most importance key elements of the proposed approaches and by highlighting some future challenges.