A new coin segmentation and graph-based identification method for numismatic application

Abstract

The automatic identification of coins from photos helps coin experts to accelerate their study of coins and to reduce the associated expenses. To address this challenging problem for numismatic applications, we propose a novel coin identification system that consists of two stages. In the first stage, an active model based segmentation approach extracts precisely the coin from the photo with its shape features; in the second stage, the coin is identified to a monetary class represented by a template coin. The similarity score of two coins is computed from graphs constructed by feature points. Validation on the USA Grading dataset demonstrates that the proposed method obtains promising results with an identification accuracy of 94.4% on 2450 coins of 148 classes.