Fake Indian Currency Detection App

Abstract

To identify counterfeit currency and report on the findings. Using a mobile camera, the model accepts the photograph. The extracted features from the scanning image are compared to a series of models. When a match is found, the outcome is outputted, indicating whether the match was true or not. Image resizing, image filtering, sobel edge detection, and template matching are the four algorithms used in this article. Even though printing false currencies is unlawful, counterfeit currencies continue to circulate in areas where there are no forms of verifying the currency's validity. The aim of this project is to avoid illicit notes from being distributed further. The project's aim is to identify false or counterfeit currency. It is accomplished by taking a sequence of steps in the same order each time. To begin, a cell phone is used to capture a picture of the currency note (camera). Second, the captured image is resized to or scaled down to 500 x 300 pixels. After that, a bilateral filter is used to eliminate noise from the signal. The features that determine a currency note's validity are then detected using the sobel operator. Correlation regression is used to match the characteristics of the note to those of an authentic note. Finally, features are listed and shown for the genuine note.