Banknote recognition by means of optimized masks, neural networks and genetic algorithms

Abstract

Previous work by the authors has proposed a banknote recognition system using a neural network (NN) to develop new types of banknote recognition machines. This system is constructed by means of some core techniques. One is a small-scale neural recognition technique using masks. The second is a mask-optimization technique using a genetic algorithm (GA). The last is a neural hardware technique using a digital signal processor (DSP). This paper focuses on and discusses the mask optimization by the GA, which is the second core technique in the neural recognition system. This technique enables the selection of good masks, that can effectively generate the characteristic values of the input image. Further, the effectiveness of this technique is shown not only by the generalization of the NN, but also by a statistical analysis, using the Italian banknotes. Finally, the feasibility and effectiveness of the neural recognition system is shown by using worldwide banknotes.