Liman Harshada,
Shikalgar Arbaj,
Chavan Aditya,
Navnath. S. Govekar,
- Student, Electronics &Telecommunication, Rajgad Dnyanpeeth’s Shree Chhatrapati Shivajiraje College of Engineering, Pune, Maharashtra, India
- Student, Electronics &Telecommunication, Rajgad Dnyanpeeth’s Shree Chhatrapati Shivajiraje College of Engineering, Pune, Maharashtra, India
- Student, Electronics &Telecommunication, Rajgad Dnyanpeeth’s Shree Chhatrapati Shivajiraje College of Engineering, Pune, Maharashtra, India
- Professor, Electronics &Telecommunication, Rajgad Dnyanpeeth’s Shree Chhatrapati Shivajiraje College of Engineering, Pune, Maharashtra, India
Abstract
Fake notes are a global issue. Indian is only one among several. Anyone may now print counterfeit notes thanks to advancements in technology. These notes are created without the state’s legal sanction, and the ongoing production of such notes could hurt a country’s economy. When counterfeit notes are created and distributed, ordinary people cannot identify the difference between real and phony money since they are identifiable by their physical qualities. The most serious issue confronting many countries, including India, is the identification of counterfeit money. Even with automatic machines in banks and other major organizations meant to discern between counterfeit currency notes, ordinary people struggle to do so. Edge detection, feature extraction, image segmentation, acquisition, grayscale conversion, and image comparison are some of the phases in the identification process.
Keywords: Machine Learning, CNN, Image Processing, Feature Extraction, Detect Currency, image extraction.
[This article belongs to Current Trends in Signal Processing(ctsp)]
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References
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Current Trends in Signal Processing
Volume | 14 |
Issue | 02 |
Received | May 27, 2024 |
Accepted | July 19, 2024 |
Published | August 20, 2024 |