Published Paper Details:

The development of color printing technology has accelerated the manufacture and duplication of false Indian rupee notes on a big scale. A few years ago, printing could only be done in a print shop, but now anyone with a cheap laser printer can print a currency note with utmost accuracy. As a result, the use of counterfeit notes in place of legitimate ones has skyrocketed. We require a technique to determine if a money note is genuine or counterfeit. We employ the Convolutional Neural Network method and image processing to determine whether a letter is genuine or a forgery. These algorithms and image processing are used to execute data processing and data extraction, resulting in an accurate result. To identify the money in the current system Is the note genuine or a forgery? It makes use of SVM (Support Vector Machine). When you have a small dataset with noise-free and labelled data, SVM is utilised for data processing. The SVM algorithm does not work well on huge datasets, and the results of the SVM algorithm are limited due to the short data set employed. When put into a real-world situation, it does not perform as well as it does now. Because of the vast data sets, the system is sluggish, has low accuracy, and consumes a lot of memory. The suggested method would evaluate the differences between a fake and a real note. Image processing introduces numerous machine learning techniques that offer the false identity of a person in the modern era of computer science and high computational approaches. the monetary unit Color, form, paper width, and image filtering on the note are among the entities detected and recognized by the algorithm. Using a Convolutional Neural Network and image processing, developing a method for detecting bogus cash. It finds the relevant traits without the need for human intervention. This technique can quickly distinguish between a fake and a genuine note. It will express the outcome in text, such as the note's worth and if it is false or genuine.