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Reseach Article

A Review Paper on Currency Recognition System

by Ami Shah, Komal Vora, Jay Mehta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 20
Year of Publication: 2015
Authors: Ami Shah, Komal Vora, Jay Mehta

Ami Shah, Komal Vora, Jay Mehta . A Review Paper on Currency Recognition System. International Journal of Computer Applications. 115, 20 ( April 2015), 1-4. DOI=10.5120/20264-2669

@article{ 10.5120/20264-2669,
author = { Ami Shah, Komal Vora, Jay Mehta },
title = { A Review Paper on Currency Recognition System },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 20 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { },
doi = { 10.5120/20264-2669 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:55:21.495850+05:30
%A Ami Shah
%A Komal Vora
%A Jay Mehta
%T A Review Paper on Currency Recognition System
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 20
%P 1-4
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

In this paper, an algorithm based on the frequency domain feature extraction method is discussed for the detection of currency. This method efficiently utilizes the local spatial features in a currency image to recognize it. The entire system is pre-processed for the optimal and efficient implementation of two dimensional discrete wavelet transform (2D DWT) which is used to develop a currency recognition system. A set of coefficient statistical moments are then extracted from the approximate efficient matrix. The extracted features can be used for recognition, classification and retrieval of currency notes. The classification result will facilitate the recognition of fake currency mainly using serial number extraction by implementing OCR. It is found that the proposed method gives superior results.

  1. Indian currency recognition based on texture analysis, INSTITUTE OF TECHNOLOGY, NIRMA UNIVERSITY, AHMEDABAD – 382 481, 08-10, IEEE DECEMBER, 2011
  2. Feature extraction of currency notes: An approach based on wavelet transform. Amir Rajaei, Elham Dallalzadeh, Mohammad Imran Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore - 570 006 ,Mysore, Karnataka, India IEEE 2012
  3. Extraction of serial number on bank notes. IEEE 2013
  4. Feature Extraction for paper currency recognition, Department of Computer and Electrical Engineering Noushirvani Institute of Technology, University of Mazandaran P. O. BOX 47144,babol,Iran ,IEEE 2007
  5. A recognition system for real time paper currency. IEEE 2012
  6. A Weighted Minimum Distance Classifier for Pattern Recognition, Department of Electtical Engineering, University of Toronto ,H. Lin and A. N. Venetsanopoulos
  7. A Fast Algorithm for the Minimum Distance Classifier and Its Application to Kanji Character Recognition,SENDA Shuji, MINOH Michihiko and IKEDA Katsuo, Department of Information Science, Kyoto University , Kyoto 606-01, Japan
  8. IEEE Standard Glossary of Image Processing and Pattern Recognition Terminology,Sponsored by the Standards Coordinating Committee of the IEEE Computer Society
  9. J. Geronimo D, Phardin PM Assopost, "Fractal functions and Wavelet expansions based on several Scaling Function" Approx. Theory, 1994, pp. 373-401.
  10. http://www. statsoft. com/textbook/support-vector-machines/
  11. Wu Chenni. DSP-based Number recognition system and achieve based on DSP. Harbin Institute of technology,2004
  12. http://www. google. com
  13. www. ieee. com
  14. http://reference. wolfram. com
Index Terms

Computer Science
Information Sciences


Feature extraction classification discrete wavelet transform textural feature currency recognition OCR fake currency security thread serial number RBI marks.