Automatic Die Studies for Ancient Numismatics

TL;DR

This paper presents a fully automatic computer vision method for die studies in ancient numismatics, significantly improving the efficiency and accuracy of analyzing Greek coin corpora compared to previous manual and semi-automatic approaches.

Contribution

The paper introduces a novel clustering-based, parameter-free approach using local descriptors for automatic die matching in ancient coins, validated on Greek coin datasets.

Findings

The proposed method outperforms previous baselines in accuracy.

It is fully automatic and does not require ground truth labels.

Validated on two Greek coin corpora.

Abstract

Die studies are fundamental to quantifying ancient monetary production, providing insights into the relationship between coinage, politics, and history. The process requires tedious manual work, which limits the size of the corpora that can be studied. Few works have attempted to automate this task, and none have been properly released and evaluated from a computer vision perspective. We propose a fully automatic approach that introduces several innovations compared to previous methods. We rely on fast and robust local descriptors matching that is set automatically. Second, the core of our proposal is a clustering-based approach that uses an intrinsic metric (that does not need the ground truth labels) to determine its critical hyper-parameters. We validate the approach on two corpora of Greek coins, propose an automatic implementation and evaluation of previous baselines, and show that our approach significantly outperforms them.