Abstract:

Imitation currency created without the state's or government's legal approval is recognized as counterfeit money. This currency is a forgery or fraud when it is produced or used. This has led to an upsurge in corruption in our nation, which has an adverse effect on its economic development. We are working on a project called "An Efficient Detection of Counterfeit Currency Using a Hybrid Framework" to combat this fraudulent practice. As soon as the note is scanned by the gadget, the suggested project will identify the counterfeit one. Methods for detection and recognition that go beyond algorithms include elements like colour, form, paper width, and note-specific picture filtering. This research suggests a technique for identifying fake currencies that includes SVM, K-Nearest Neighbour, Decision Tree, AdaBoost, GentleBoost and Bagging with image processing. These datasets for banknote authentication was developed using sophisticated computational and mathematical techniques that provide accurate data and information on the entities and properties associated to cash. Using machine learning algorithms and image processing, data processing and data extraction are carried out to obtain the desired outcome and accuracy

Description:

Features extraction, K-Nearest Neighbour, Image processing, Accuracy, Data extraction.