Counterfeit currency detection using machine learning

Abstract

This paper addresses the pervasive issue of counterfeit currency through a comprehensive approach integrating advanced image processing techniques and machine learning algorithms. The methodology encompasses crucial stages, including image comparison, segmentation, edge detection, feature extraction, and grayscale conversion, coupled with the implementation of machine learning models such as K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN) and the efficient MobileNetV2. In tackling the challenge of counterfeit currency, image processing techniques play a pivotal role by enabling the extraction and analysis of distinct features. From isolating patterns through segmentation to refining with edge detection and feature extraction, these techniques enhance the identification of intricate characteristics inherent in legitimate banknotes. Grayscale conversion further standardizes the representation for effective processing.