Banknote recognition based on optimization of discriminative regions by genetic algorithm with one-dimensional visible-light line sensor

Banknote recognition is an important task in many automatic payment facilities and counting machines. The most popular approach is based on image processing methods in which banknote images are captured by visible light sensors and are classified by denominations and input orientations. There are regions on a banknote image that yield better recognition accuracy than the other areas. There have been few studies on optimal discriminative regions on a banknote image; therefore, we proposed a banknote recognition method to select the discriminative regions on the banknote image captured by a one-dimensional visible light sensor. The proposed method uses genetic algorithm to optimize the similarity mapping result for different classes of banknotes. Experimental results with banknote databases from various countries show that our proposed method results in better accuracies than previous methods with the average recognition accuracies of higher than 99% and small variance among five trials in each type of currency.