FAKE CURRENCY DETECTION USING SIMPLE IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUES
International Research Journal of Modernization in Engineering Technology and Science · 2023
The automation of technology results in a more extensively duplicated currency, which slows the expansion of the nation's economy.Note detection must be very constant and dependable in order to be required.Edge detection, extraction of features, segmentation techniques, sepia conversion, and image comparison are some of the procedures involved in the identification of paper money.This essay also includes a review of the literature on various detection strategies.Whenever we use some effective preparation and feature extraction approaches, it assists in refining the algorithms and also the detection method, according to the review to detect malpractice.Machine learning techniques assist in creating the tools needed and required for research work, and we can create computer learning designs, implementations, and strategies to distinguish between counterfeit and real money.We have suggested employing a deep neural network based on convolution to detect fake money.Through analysis of the currency photos, our technique detects counterfeit money.To learn the feature map of the currencies, the transfer learnt convolutional neural networks is trained using a total of two thousand currency note set data.The network is prepared to recognize fraudulent currencies in real time after the feature map has been learned.