Advertisement

Integrity Checking of Physical Currency with Pattern Matching: Coping with Few Data and the Training Sample Order

  • Ch. RupaEmail author
  • T. Sumanth
Original Contribution
  • 5 Downloads

Abstract

Nowadays, technology utilization increases rapidly in our daily life. Detection of original things from fake make difficult due to misusers of this technology. Currently, world is moving toward digitalization even though physical currency is playing an important role in some of the rural areas. Counterfeit detection and maintain is a major issue. In this paper, an approach has proposed that would be useful to recognize the identity of the currencies and decreases the processing time of verification by pattern recognition techniques. This method can improves accuracy in the process of fake notes detection. In order to recognize currency features, this system uses preprocessing, color detection, segmentation, edge detection and template matching techniques. The result of this application notifies that whether the currency is original or fake to the end user. Here, we have been considering Indian currency dataset for testing the proposed system. The main strengths of this paper are performance analysis which has done by various approaches like quantitative method histogram and quality metrics comparison with test case study reports.

Keywords

Currency note Segmentation Edge detection Pattern recognition Confusion matrix Histogram Quality metrics 

Notes

References

  1. 1.
    S. Kamal, S.S. Chawla, N. Goel, B. Raman, Feature extraction and identification of Indian currency notes. Int. J. Eng. Comput. Sci. 5(7), 1–4 (2017)Google Scholar
  2. 2.
    G.R.Gidveer, Sonali R. Darade, in Automatic Recognition of Fake Indian currency Note, IEEE International Conference on Electrical Power and Energy Systems, 2017Google Scholar
  3. 3.
    J. Chambers, W. Yan, A. Garhwal, M. Kankanhalli, Currency security and forensics:a survey. J. Multimed. Tools Appl. ACM 74(11), 4013–4043 (2015)CrossRefGoogle Scholar
  4. 4.
    N. Rathee, A. Kadian, R. Sachdeva, in Feature Fusion for Fake Indian currency Detection Computing for Sustainable Global Development (INDIACom), 3rd International Conference on: IEEE, 2016Google Scholar
  5. 5.
    M. Sarfraza, in An Intelligent Paper Currency Recognition System, International Conference on Communication, Management and Information Technology, 2015Google Scholar
  6. 6.
    R.C. Gonzalez, R.E. Woods, Digital image processing (Interscience, New York, 2003)Google Scholar
  7. 7.
    A. Roy, B. Halder, U. Garain, D.S. Doermann, Machine-assisted authentication of paper currency: an experiment on Indian banknotes. J. Eng. Sci. 18, 271–285 (2015)Google Scholar
  8. 8.
    Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, Sei-Wan Chen, Automatic license plate recognition. IEEE Trans. Intell. Trans. Syst. 5(1), 42–53 (2010)CrossRefGoogle Scholar
  9. 9.
    Y. Liu, L. Zeng, Haar-svm for real-time banknotes recognition. J. Inf. Comput. Sci. 11(12), 4031–4039 (2014)CrossRefGoogle Scholar
  10. 10.
    Syndicate Bank Manager, Kanuru (Vijayawada, Andhrapradesh, 2017)Google Scholar
  11. 11.
    R. Nagpure, S. Sheety, T. Ghotkar, Currency recognition and fake note detection. IJIRCCE 4, 3659–3666 (2016)Google Scholar
  12. 12.
    A.B. Sargano, M. Sarfrazb, N. Haq, An intelligent system for paper currency recognition with robust features. J. Intell. Fuzzy Syst. 27, 1905–1913 (2014)Google Scholar
  13. 13.
    S. Murthy, J. Kurumathur, B. Roja Reddy, Design and implementation of paper currency recognition with counterfeit detection. IEEE international conference on green engineering technologies (2016).  https://doi.org/10.1109/GET.2016.7916838
  14. 14.
    V. Abuuru, et.al, in Currency Recognition system using Image Processing, IEEE International Conference on Contemporary Computing 2017Google Scholar

Copyright information

© The Institution of Engineers (India) 2019

Authors and Affiliations

  1. 1.Department of CSEVelagapudi Ramakrishna Siddhartha Engineering CollegeVijayawadaIndia

Personalised recommendations