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Fast and Efficient Coin Recognition using 5 Hidden Layers BPNN

Saloni Shrivastava1 , Bhupesh Gour2

  1. Dept. of CSE, Lakshmi Narain College of Technology (RGPV University), Bhopal, India.
  2. Dept. of CSE, Lakshmi Narain College of Technology (RGPV University), Bhopal, India.

Correspondence should be addressed to: salonishrivastava8@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-9 , Page no. 122-127, Sep-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i9.122127

Online published on Sep 30, 2017

Copyright © Saloni Shrivastava, Bhupesh Gour . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Saloni Shrivastava, Bhupesh Gour, “Fast and Efficient Coin Recognition using 5 Hidden Layers BPNN,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.122-127, 2017.

MLA Style Citation: Saloni Shrivastava, Bhupesh Gour "Fast and Efficient Coin Recognition using 5 Hidden Layers BPNN." International Journal of Computer Sciences and Engineering 5.9 (2017): 122-127.

APA Style Citation: Saloni Shrivastava, Bhupesh Gour, (2017). Fast and Efficient Coin Recognition using 5 Hidden Layers BPNN. International Journal of Computer Sciences and Engineering, 5(9), 122-127.

BibTex Style Citation:
@article{Shrivastava_2017,
author = {Saloni Shrivastava, Bhupesh Gour},
title = {Fast and Efficient Coin Recognition using 5 Hidden Layers BPNN},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2017},
volume = {5},
Issue = {9},
month = {9},
year = {2017},
issn = {2347-2693},
pages = {122-127},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1441},
doi = {https://doi.org/10.26438/ijcse/v5i9.122127}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v5i9.122127}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1441
TI - Fast and Efficient Coin Recognition using 5 Hidden Layers BPNN
T2 - International Journal of Computer Sciences and Engineering
AU - Saloni Shrivastava, Bhupesh Gour
PY - 2017
DA - 2017/09/30
PB - IJCSE, Indore, INDIA
SP - 122-127
IS - 9
VL - 5
SN - 2347-2693
ER -

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Abstract

Coins have been integral a part of our day to day life. Coins are used nearly every place like in grocery stores, banks, trains, buses etc. Thus it`s a basic would like that coins may be recognized, counted, sorted mechanically. For this, it is necessary that coins can be recognized automatically and check whether it’s real or fake In this paper we have developed an ANN Fast and Efficient coin recognition using 5 hidden layers Back-Propagation Neural Networks Algorithm for the recognition of Indian Coins of denomination `1, `2, `5 and `10 using Canny Edge Detection. We have taken images from both sides of the coin. So this system is capable of recognizing coins from both sides. Features are extracted from images using techniques of Labeling, Canny Edge Detection, and Image Processing etc. Then, the extracted features are passed as input to a trained Neural Network 84.3% recognition rate has been achieved during the experiments.

Key-Words / Index Term

Canny edge detection, BP neural network, coin recognition, Labeling, Image Processing

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