SerendibCoins: Exploring The Sri Lankan Coins Dataset

TL;DR

This paper introduces a new Sri Lankan coin image dataset and evaluates various machine learning models, demonstrating that CNNs achieve near-perfect accuracy, significantly advancing automated coin recognition systems.

Contribution

It provides a comprehensive Sri Lankan coin dataset and benchmarks multiple classifiers, highlighting CNN's superior performance for coin classification tasks.

Findings

SVM outperforms KNN and Random Forest in traditional methods

CNN achieves near-perfect classification accuracy

Dataset enhances automated coin recognition systems

Abstract

The recognition and classification of coins are essential in numerous financial and automated systems. This study introduces a comprehensive Sri Lankan coin image dataset and evaluates its impact on machine learning model accuracy for coin classification. We experiment with traditional machine learning classifiers K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest as well as a custom Convolutional Neural Network (CNN) to benchmark performance at different levels of classification. Our results show that SVM outperforms KNN and Random Forest in traditional classification approaches, while the CNN model achieves near-perfect classification accuracy with minimal misclassifications. The dataset demonstrates significant potential in enhancing automated coin recognition systems, offering a robust foundation for future research in regional currency classification and deep learning applications.