Recognition of Counterfeit Currency Using OpenCV and Python

Authors

  • A. Akshatha Student, Department of Computer Science & Engineering, SJB Institute of Technology, Bangalore, India
  • Amruthavalli Punuganti Student, Department of Computer Science & Engineering, SJB Institute of Technology, Bangalore, India
  • Bhoomika Panduranga Student, Department of Computer Science & Engineering, SJB Institute of Technology, Bangalore, India
  • Chaitanya Girish Student, Department of Computer Science & Engineering, SJB Institute of Technology, Bangalore, India

Keywords:

Characteristic extraction, Comparing images, Cross platform, Flutter plugin, Image Processing

Abstract

The Currency Recognition System was developed for the purpose of fraud detection in paper currency, so this system is used worldwide. The uses of this framework can be recognized in banking frameworks, cash observing gadgets, cash trade frameworks, and so forth. This framework is significant in the advanced universe of today and should be available easily in hand. This paper proposes an automatic paper currency recognition system through an application developed. The algorithm implemented is simple, robust and efficient. The methodology comprises of various segments including picture preparing, characteristic extraction and contrasting test pictures and the prepared ones. The result produced consists of the denomination of the currency, whether it is actual or forged note and translation of the currency to other countries’ currency. The aim is also to develop a mobile app using the flutter plugin. Flutter is a cross- platform for creating both Android and iOS-based applications. The mobile app created here is well-designed and user-friendly. The proposed system’s performance has been calculated providing challenging dataset with different constraints. The app is accurate, fast, reliable and low cost in comparison with currently available systems.

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Published

01-08-2020

How to Cite

[1]
A. Akshatha, A. . Punuganti, B. . Panduranga, and C. . Girish, “Recognition of Counterfeit Currency Using OpenCV and Python”, IJRESM, vol. 3, no. 7, pp. 403–408, Aug. 2020.

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Articles