Indian Currency Recognition for Visually Challenged using Machine Learning and Deep Learning

Authors

  • Nijil Raj N Dept. of Computer Science and Engineering, Younus College of Engineering and Technology, Kollam, Kerala, India
  • Anandu S. Ram Dept. of Computer Science and Engineering, Younus College of Engineering and Technology, Kollam, Kerala, India
  • Aneeta Binoo Joseph Dept. of Computer Science and Engineering, Younus College of Engineering and Technology, Kollam, Kerala, India
  • Shabna S Dept. of Computer Science and Engineering, Younus College of Engineering and Technology, Kollam, Kerala, India

DOI:

https://doi.org/10.26438/ijcse/v8i7.116121

Keywords:

Banknote Recognition, Deep Learning, Machine Learning

Abstract

Vision Impairment has been treated as a deterrent to normal functioning in human beings, so for such people, it is difficult to recognize the notes. The current system uses Malaysian Ringgit banknotes and extracts RGB values from the banknotes. The algorithms used were KNN, SVM, Naive Bayes, Decision Tree, and deep learning Alexnet. The proposed system uses Indian Currency and is divided into 3 phases. In phase I four features are extracted, phase II RGB values are extracted, and finally, in phase III, phase I and phase II are concatenated to produce better results. The algorithms used are KNN, Decision tree, SVM, Naive Bayes and deep learning VGGnet. Our system provides an accuracy of 98 percent in KNN, 95 percent in Decision Tree, 100 percent in SVM and 90 percent in Naive Bayes.

References

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Published

2020-07-31
CITATION
DOI: 10.26438/ijcse/v8i7.116121
Published: 2020-07-31

How to Cite

[1]
N. R. N, A. S. Ram, A. B. Joseph, and S. S, “Indian Currency Recognition for Visually Challenged using Machine Learning and Deep Learning”, Int. J. Comp. Sci. Eng., vol. 8, no. 7, pp. 116–121, Jul. 2020.

Issue

Vol. 8 No. 7 (2020): IJCSE July Edition

Section

Research Article

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