A currency recognition system using negatively correlated neural network ensemble

This paper represents a currency recognition system using ensemble neural network (ENN). The individual neural networks (NN) in an ENN are trained via negative correlation learning. The object of using negative correlation learning (NCL) is to expertise the individuals in an ensemble on different parts or portion of input patterns. 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. We performed our experiment for seven different types of TAKA (Bangladeshi currency) they are 2, 5, 10, 20, 50, 100 and 500 TAKA. The image of different types note is converted in gray scale and compressed in our 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 currency. It reduces the chances of misclassification than a single network and ensemble network with independent training. In experimental results we have shown this. We also find good result for similar pattern available in market.