Automated Currency Detection Using Feature Extraction and Hybrid Classification Models

Abstract

The circulation of counterfeit currency poses a serious threat to economic stability, making accurate detection essential. This paper presents a hybrid approach combining image processing, machine learning algorithms, and YOLO-based deep learning for effective fake currency detection. The system extracts features such as texture, shape, and color, and applies classification models to distinguish genuine and counterfeit notes with high accuracy. Experimental results demonstrate improved performance, reliability, and real-time detection capability compared to traditional methods.