A paper currency recognition method by a small size neural network with optimized masks by GA

Compactness, transaction speed, and cost are important design factors when we apply neural networks to commercial products. We propose a structure reduction method for NNs. We adopt slab values which are sums of input pixels as characteristics of the inputs. But there is the possibility of generating the same slab values even when the inputs are different. To avoid this problem, we adopt a mask which covers some parts of the input. This enables us to reflect the difference of input pattern to slab values with masks. Furthermore, we adopt the genetic algorithm (GA) to optimize the masks. We can generate various effective masks automatically. Finally, we show that the proposed method by neuro-recognition with masks can be applied effectively to paper currency recognition machine using the GA