Literature Survey on Revolutionizing Fake Currency Detection: CNN-Based Approach for Indian Rupee Notes

Abstract

The proliferation of counterfeit currency poses a significant challenge in various economies, including India. To address this issue, several studies have proposed innovative image processing and machine learning techniques for detecting counterfeit coins and banknotes. Leveraging digital image processing, these studies aim to enhance the security measures against counterfeit currency through accurate and efficient recognition systems. Techniques such as preprocessing, segmentation, feature extraction, and clustering are employed to identify fraudulent currency. The use of Support Vector Machines (SVM), k-means clustering, and Convolutional Neural Networks (CNN) facilitates the recognition and classification of genuine and counterfeit currency. Moreover, these studies explore the application of spatial coding, component-based recognition, and deep learning algorithms to improve the accuracy and robustness of counterfeit detection systems. By developing real-time recognition systems capable of identifying counterfeit currency, these research efforts contribute to combating financial fraud and safeguarding economic integrity.