MACHINE LEARNING APPROACH FOR HYBRID FAKE CURRENCY DETECTION SYSTEM

Abstract: Malpractice has always been a serious challenge that results in problems for society. The increasing use of technology has led to an increase in counterfeit currency, which negatively impacts a country's economic growth. Therefore, it is crucial to have reliable and consistent note detection. The process of identifying paper currency involves several steps such as edge detection, feature extraction, image segmentation, grayscale conversion, and image comparison. This paper includes a literature survey that presents different methodologies for detection. The review concludes that applying efficient preprocessing and feature extraction techniques improves the algorithm and the detection system. Machine Learning techniques help in building tools that are necessary for research work, allowing us to develop computer learning design, implementation, and methods to differentiate between fake and genuine currency. The utilization of pattern recognition and image processing learning and analyzing methods helps to identify distinguishing features.