From Coin to Data: The Impact of Object Detection on Digital Numismatics

TL;DR

This paper explores how advanced object detection models, especially CLIP, can improve the analysis and classification of historical coins in digital numismatics, enhancing research and artifact verification.

Contribution

It introduces a flexible framework using CLIP for coin feature detection and classification, with a statistical calibration method for low-quality datasets, advancing digital numismatics techniques.

Findings

Larger CLIP models outperform traditional methods in complex imagery detection.

Traditional methods excel in simple geometric pattern recognition.

Calibration improves reliability of similarity scores in degraded datasets.

Abstract

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.