World Coin Currency Detection: A Comparative Study of Machine Learning and Deep Learning

In the economy where trade and finance are closely linked it is essential to accurately identify and authenticate various currencies, especially in industries such, as banking, tourism and retail. Conventional methods, for identifying coins typically require inspections or simple automated processes, which might take up a lot of time and are susceptible, to mistakes. The advancements, in machine learning (ML) and deep learning (DL) technologies, over the years have revolutionized currency detection providing efficient solutions. This article discusses the evaluation of machine learning and deep learning approaches to identify types of coins. It explores how algorithms, like MobileNetV2, Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) perform in distinguishing and classifying coin denominations across currencies. The results of experiments conducted on data sets show how these methods compare in terms of accuracy, effectiveness, and reliability. Furthermore, we examine the advantages and constraints of each approach while suggesting directions for advancing research, in the realm of identifying cryptocurrency.