Fake Indian Currency Detection
Aneena Babu1, Vineetha Shankar P2
1Aneena Babu, Department of Computer Science, St. Albert’s College, Kochi (Kerala), India.
2Vineetha Sankar P, Department of Computer Science, St. Albert’s College, Kochi (Kerala), India.
Manuscript received on 25 April 2024 | Revised Manuscript received on 09 May 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024 | PP: 21-25 | Volume-4 Issue-1 May 2024 | Retrieval Number: 100.1/ijdm.A164004010524 | DOI: 10.54105/ijdm.A1640.04010524
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The proliferation of counterfeit currency poses a significant threat to both individuals and the national economy. While existing fake currency detection tools are primarily accessible to banks and large enterprises, everyday people and small businesses remain susceptible. Thus, this project aims to delve into the security features of Indian currency and develop a software solution leveraging advanced image processing and computer vision techniques to detect and neutralize counterfeit notes. Counterfeiting currency poses a genuine menace to both the populace’s well-being and the nation’s economic stability. Although counterfeit currency detection tools exist, their accessibility is typically confined to banking institutions and corporate entities, leaving ordinary citizens and small enterprises susceptible to fraud. Thus, this project endeavours to examine the diverse security attributes of Indian currency and subsequently craft a software-driven apparatus capable of discerning and nullifying counterfeit Indian currency through sophisticated image processing and computer vision methodologies. Notably, this currency authentication system will be meticulously crafted using the Python programming language within the Jupyter Notebook framework.
Keywords: Fake Currency, Counterfeit Detection, Image Processing, Feature Extraction, Brute Force Matcher, ORB Detector.
Article of the Scope: Data Science