A Paper Currency Recognition System Using Negatively Correlated Neural Network Ensemble

Abstract

This paper presents a currency recognition system using ensemble neural network (ENN). The individual neural networks (NNs) in an ENN are trained via negative correlation learning. The objective of using negative correlation learning (NCL) is to expertise the individuals on different parts or portion of input patterns in an ensemble. The available currencies in the market consist of new, old and noisy ones. It is often difficult for machine to recognize these currencies; therefore we propose a system that uses ENN to identify them efficiently. We performed our experiment for seven different types of TAKA (Bangladeshi currency) which are 2, 5, 10, 20, 50, 100 and 500 TAKA. The image of different types note is converted in gray scale and compressed in the desired range. Each pixel of the compressed image is given as an input to the network. This system is able to recognize highly noisy or old image of TAKA. Ensemble network is very useful for the classification of different types of currencies. It reduces the chances of misclassification than a single network and ensemble network with independent training. In experimental results we have shown this with artificially added noise in the test set. We also find good result for similar pattern available in market.