Reliable Banknote Classification Using Neural Networks

We present a method based on principal component analysis (PCA) for increasing the reliability of banknote recognition. The system is intended for classifying any kind of currency, but in this paper we examine only US dollars (six different bill types). The data was acquired through an advanced line sensor, and after preprocessing, the PCA algorithm was used to extract the main features of data and to reduce the data size. A linear vector quantization (LVQ) network was applied as the main classifier of the system. By defining a new method for validating the reliability, we evaluated the reliability of the system for 1,200 test samples. The results show that the reliability is increased up to 95% when the number of PCA components is taken properly as well as the number of LVQ codebook vectors. In order to compare the results of classification, we also applied hidden Markov models (HMMs) as an alternative classifier.