Fast and Efficient Coin Recognition using 5 Hidden Layers BPNN
DOI:
https://doi.org/10.26438/ijcse/v5i9.122127Keywords:
Canny edge detection, BP neural network, coin recognition, Labeling, Image ProcessingAbstract
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.
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