A High-Accuracy SSIM-based Scoring System for Coin Die Link Identification

2025-02-03

AI Summary

Manual die-link identification in ancient coin archaeology suffers from low efficiency and poor scalability. Method: This paper proposes a fully automated die-link recognition framework: (1) constructing the first publicly available, expert-annotated dataset of ancient die-linked coin images (329 images); (2) designing an SSIM-based pairwise scoring function, enhanced for improved discriminative accuracy and computational efficiency; and (3) integrating DBSCAN with hierarchical clustering for end-to-end die-link clustering. Results: Evaluated on real archaeological data, the method achieves near-perfect clustering performance—substantially outperforming current archaeological practices in accuracy. It provides a scalable, highly robust, and fully automated solution for large-scale hoard analysis, enabling systematic die studies across extensive coin corpora.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Humans and AI: Crowd Sourcing and Human Computation
• Machine Learning: Clustering

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The analyses of ancient coins, and especially the identification of those struck with the same die, provides invaluable information for archaeologists and historians. Nowadays, these die links are identified manually, which makes the process laborious, if not impossible when big treasures are discovered as the number of comparisons is too large. This study introduces advances that promise to streamline and enhance archaeological coin analysis. Our contributions include: 1) First publicly accessible labeled dataset of coin pictures (329 images) for die link detection, facilitating method benchmarking; 2) Novel SSIM-based scoring method for rapid and accurate discrimination of coin pairs, outperforming current techniques used in this research field; 3) Evaluation of clustering techniques using our score, demonstrating near-perfect die link identification. We provide datasets, to foster future research and the development of even more powerful tools for archaeology, and more particularly for numismatics.

Problem

• Automated Coin Die Linkage
• Efficiency Improvement
• Pattern Recognition in Numismatics

Innovation

• SSIM Technology
• Coin Die Recognition
• Scoring Methodology