From Coin to Data: The Impact of Object Detection on Digital Numismatics
This work addresses the need for scalable and precise analysis in digital numismatics, enabling advancements in cultural heritage research, though it is incremental as it adapts existing methods to a new domain.
The paper tackled the problem of analyzing historical coins by applying object detection techniques, showing that larger CLIP models perform better for complex imagery while traditional methods excel with simple patterns, and proposing a calibration mechanism for low-quality datasets.
In this work we investigate the application of advanced object detection techniques to digital numismatics, focussing on the analysis of historical coins. Leveraging models such as Contrastive Language-Image Pre-training (CLIP), we develop a flexible framework for identifying and classifying specific coin features using both image and textual descriptions. By examining two distinct datasets, modern Russian coins featuring intricate "Saint George and the Dragon" designs and degraded 1st millennium AD Southeast Asian coins bearing Hindu-Buddhist symbols, we evaluate the efficacy of different detection algorithms in search and classification tasks. Our results demonstrate the superior performance of larger CLIP models in detecting complex imagery, while traditional methods excel in identifying simple geometric patterns. Additionally, we propose a statistical calibration mechanism to enhance the reliability of similarity scores in low-quality datasets. This work highlights the transformative potential of integrating state-of-the-art object detection into digital numismatics, enabling more scalable, precise, and efficient analysis of historical artifacts. These advancements pave the way for new methodologies in cultural heritage research, artefact provenance studies, and the detection of forgeries.