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Reseach Article

Automated Coin Recognition System using ANN

by Shatrughan Modi, Dr. Seema Bawa
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 26 - Number 4
Year of Publication: 2011
Authors: Shatrughan Modi, Dr. Seema Bawa
10.5120/3093-4244

Shatrughan Modi, Dr. Seema Bawa . Automated Coin Recognition System using ANN. International Journal of Computer Applications. 26, 4 ( July 2011), 13-18. DOI=10.5120/3093-4244

@article{ 10.5120/3093-4244,
author = { Shatrughan Modi, Dr. Seema Bawa },
title = { Automated Coin Recognition System using ANN },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 26 },
number = { 4 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 13-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume26/number4/3093-4244/ },
doi = { 10.5120/3093-4244 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:11:56.060588+05:30
%A Shatrughan Modi
%A Dr. Seema Bawa
%T Automated Coin Recognition System using ANN
%J International Journal of Computer Applications
%@ 0975-8887
%V 26
%N 4
%P 13-18
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Coins are integral part of our day to day life. We use coins everywhere like grocery store, banks, buses, trains etc. So it becomes a basic need that coins can be sorted and counted automatically. For this it is necessary that coins can be recognized automatically. In this paper we have developed an ANN (Artificial Neural Network) based Automated Coin Recognition System for the recognition of Indian Coins of denomination `1, `2, `5 and `10 with rotation invariance. We have taken images from both sides of coin. So this system is capable of recognizing coins from both sides. Features are extracted from images using techniques of Hough Transformation, Pattern Averaging etc. Then, the extracted features are passed as input to a trained Neural Network. 97.74% recognition rate has been achieved during the experiments i.e. only 2.26% miss recognition, which is quite encouraging.

References
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  7. Roushdy, M., “Detecting Coins with Different Radii based on Hough Transform in Noisy and Deformed Image”, In the proceedings of GVIP Journal, Volume 7, Issue 1, April, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Pattern Averaging Hough Transform for circle detection Automated Coin Recognition