EigenCoin: sassanid coins classification based on Bhattacharyya distance

Rahele Allahverdi; Mohammad Mahdi Dehshibi; Azam Bastanfard; Daryoosh Akbarzadeh

cs.CVApr 13, 2026

EigenCoin: sassanid coins classification based on Bhattacharyya distance

Authors: Rahele Allahverdi, Mohammad Mahdi Dehshibi, Azam Bastanfard, Daryoosh Akbarzadeh

Organizations: Department of Electrical, Computer and IT, Islamic Azad University, Qazvin Branch, Iran · Department of Computer Engineering, Islamic Azad University, Karaj Branch, Iran · National Museum of Iran, Tehran, Iran

Abstract

Solving pattern recognition problems using imbalanced databases is a hot topic, which entices researchers to bring it into focus. Therefore, we consider this problem in the application of Sassanid coins classification. Our focus is not only on proposing EigenCoin manifold with Bhattacharyya distance for the classification task, but also on testing the influence of the holistic and feature-based approaches. EigenCoin consists of three main steps namely manifold construction, mapping test data, and classification. Conducted experiments show EigenCoin outperformed other observed algorithms and achieved the accuracy from 9.45% up to 21.75%, while it has the capability of handling the over-fitting problem.

Image Classification

Image classification assigns semantic labels to images, enabling recognition of objects, scenes, conditions, and fine-grained categories.

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Reading Legends on Ancient Coins: An Object Detection Approach for Character Recognition on a Novel Roman Republican Dataset

Hafeez Anwar

Department of Computer Science, National University of Computer and Emerging Sciences (FAST-NUCES), Peshawar, 25000, Pakistan

Object Detection Optical Character Recognition

Open paper AskPDF arXiv

When it comes to the proper classification of ancient coins with respect to their time and issuer, the textual inscriptions on these coins, also known as legends, are of paramount importance. These legends consist of alphabets or characters still used in English. This paper addresses image based character recognition on ancient Roman Republican coins via a deep learning based object detection strategy. However, legends on these coins pose high variation due to non-uniform placement, primitive inscription techniques, and wear and tear. Additional challenges include inconsistent imaging conditions such as illumination, orientation, and scale. To accommodate these, we gathered a novel large-scale dataset of 5,654 Roman Republican coin images, manually annotated with 21 character labels, totaling 38,808 annotations. For recognition, we use You Only Look Once (YOLO) variants: YOLOv3, v4, v5, v7, and v8. YOLOv7-Large achieves the best mAP50 of 90.4%, followed by YOLOv7-Extended and YOLOv7-xl with 90.2% and 90.1%, respectively.

Abstract

Image Classification

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