Characteristics extraction of paper currency using symmetrical masks optimized by GA and neuro-recognition of multi-national paper currency

We have researched a neural network (NN) recognition method and developed a hardware for paper currency. We have proposed a mask concept to extract characteristics of the paper currency. Furthermore, we have adapted a genetic algorithm (GA) to a mask optimization. We propose a unique mask which has a symmetrical masked area against an axis which divides a long side of the currency, equally. We can obtain the same value from both an upright image and an inverse one of the currency through the mask processor using the axis-symmetrical mask. This means these values are invariant to upright and inverse of the currency conveyance. First we show the geometrical meaning of the axis-symmetrical mask and show the procedure of the their optimization by the GA using Japanese, Italian, Spanish, and French currency. Then we show realization of multi-national currency recognition. Finally, we implement this mask on a neuro-banking machine and discuss the effectiveness using a large quantity of the currency