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

2024-12-26

AI Summary

This work addresses the low accuracy and poor generalizability in fine-grained feature identification of historical coins—particularly severely degraded Southeast Asian Hindu-Buddhist coins and modern Russian Saint George coins. We propose a multimodal analysis framework integrating visual and textual semantics. To our knowledge, this is the first study to adapt large vision-language models (e.g., CLIP) for coin fine-grained detection, augmented by a statistical similarity calibration mechanism tailored for low-quality images, thereby significantly enhancing robustness in cross-domain and cross-quality matching scenarios. Through multimodal feature alignment and comparative evaluation against conventional object detection methods, experiments demonstrate that CLIP substantially outperforms YOLOv8 in complex pattern localization. The proposed calibration mechanism improves classification accuracy on low-quality datasets by 12.7%, effectively supporting high-precision dating, authenticity verification, and cultural provenance analysis.

Technology Category

• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Historical Coin Features
• Advanced Object Detection
• Digital Numismatics

Innovation

• CLIP models
• statistical calibration
• digital numismatics