Fast Country Classification of Banknotes

In this paper, we present a fast algorithm for country classification of banknotes. The algorithm can be used as an initial step for conventional banknote classification methods developed for a single currency in multi-country environment. We assume that the input image is a Contact Image Sensor (CIS) scan image with de-skewing and Region of Interest (ROI) extraction. In the training process, after size normalization we extract eigenimage for a banknote group based on overall context similarity. With the dominant eigenimage of each banknote group, we compute correlation metrics between the dominant eigenimage and test images. We tested the algorithm with four currencies: USD, KRW, CNY and EUR. The proposed method shows 100% accuracy and it took about 0.37ms for a banknote.