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@article{152854,
        author = {KURELA ANANDA KUMAR and Mr. ANJI BABU},
        title = {FAKE INDIAN CURRENCY RECOGNITION SYSTEM WITH CONTRAST-LIMITED ADAPTIVE HISTOGRAM EQUALIZATION USING MATLAB},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {4},
        pages = {624-628},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152854},
        abstract = {This research created a computer vision-based method for detecting Indian paper cash. In this approach, currency features are extracted and custom datasets are created for currency detection. The front and back side security features of the Rs. 200 denomination Indian currency note were extracted using a feature extraction method. The ORB (Oriented FAST and Rotated BRIEF) and Brute-Force matcher approaches are primarily used. so that the obverse and reverse denominations of banknotes may be detected more correctly Our significant contribution is that we tested this method on several denominations of Indian banknotes using ORB and BF matchers in OpenCV. The average accuracy of detection is up to 95.0 percent.},
        keywords = {Fake Currency, KNN, ORB, DOG, SIFT},
        month = {},
        }