International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-12 Issue-132 November-2025
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Identification of Indian currency note authenticity using ensemble learning and image processing

Vivek Sharan11, Amandeep Kaur2 and Parvinder Singh3

Research Scholar, Department of Computer Science and Technology,Central University of Punjab, Bathinda,Punjab,India1
Professor, Department of Computer Science and Technology,Central University of Punjab, Bathinda,Punjab,India2
Assistant Professor, Department of Computer Science and Technology,Central University of Punjab, Bathinda,Punjab,India3
Corresponding Author : Vivek Sharan1

Recieved : 09-October-2024; Revised : 26-November-2025; Accepted : 27-November-2025

Abstract

Technology has become an integral part of modern life, influencing nearly every aspect of society. Despite its numerous advantages, certain challenges persist—one of the most significant being the circulation of counterfeit currency. With the advancement of printing and scanning technologies, counterfeiters can now produce fake currency that closely resembles genuine notes, posing a serious threat to economic stability. Such counterfeit currency often circulates globally without legal authorization, and India is among the countries affected by this issue. Although several studies have attempted to address this problem, further improvement in detection accuracy and robustness remains necessary. Feature extraction from currency images presents a considerable challenge, particularly when the currency is damaged, worn, or poorly illuminated. To address this, a voting ensemble learning model (VELM) integrated with image processing techniques is proposed, where support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT) act as base learners for classifying genuine and counterfeit Indian currency. The dataset comprises 2,335 images of Indian banknotes (denominations of ₹200, ₹500, and ₹2000), which were augmented to a total of 22,153 images. These samples include various conditions such as non-uniform illumination, different orientations, handwritten markings, dirt, tears, and wrinkles. Several image preprocessing steps were applied to extract relevant texture features, followed by a dataset split with a 70:30 train-test ratio. The model was trained using 70% of the data and evaluated on the remaining 30%. Experimental results demonstrate that the proposed ensemble model effectively distinguishes between real and counterfeit currency with high accuracy, outperforming individual classifiers such as SVM, KNN, and DT.

Keywords

Counterfeit currency detection, Image processing, Voting ensemble learning model (VELM), Support vector machine (SVM), K-nearest neighbor (KNN), Decision tree (DT).

Cite this article

Sharan1 V, Kaur A, Singh P. Identification of Indian currency note authenticity using ensemble learning and image processing. International Journal of Advanced Technology and Engineering Exploration. 2025;12(132):1768-1782. DOI : 10.19101/IJATEE.2024.111101852

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