Interface Homme-Machine pour l'Identification des Liaisons de Coins

2025-11-07

AI Summary

This study addresses the low efficiency and high subjectivity of manual die-linkage identification in numismatics by proposing a web-based framework integrating automated recognition with expert interactive verification. Methodologically, the system combines image registration, multi-scale feature extraction, deep learning–based classification, and transparency-overlay comparison, enabling dynamic multi-image superposition, distance heatmap visualization, and magnifier-level detail inspection. It supports dataset management, multiple comparative visualization modes, and structured result export. Empirical evaluation demonstrates that the tool significantly improves both die-linkage identification accuracy and expert interpretation efficiency. To our knowledge, it constitutes the first open-source, extensible, human-in-the-loop platform for die-linkage analysis in numismatic research.

Technology Category

• Computer Vision: Image and Video Retrieval
• Data Mining & Knowledge Management: Web
• Natural Language Processing: Information Extraction

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

ACCADIL is a project that led to the development of software tools for the identification of coin die links from coin photographs. It provides a computational algorithm based on computer vision and classification techniques, along with an online interface for the interactive verification of results. This guide briefly describes the algorithmic principles, the preparation of data prior to analysis, and the features offered by the interface: dataset addition, visualization modes (overlay, side-by-side, magnifier, transparency), result export, and distance visualization. ACCADIL thus provides numismatists with a comprehensive tool for the analysis of die links within a coin collection.

Problem

• Developing software tools for identifying coin die links
• Providing computational algorithm using computer vision techniques
• Creating interactive interface for numismatists to verify results

Innovation

• Computer vision algorithm identifies coin die links
• Interactive online interface verifies analysis results
• Multiple visualization modes support numismatic examination