Toshihiro Nishikage

Multiple kinds of paper currency recognition using neural network and application for Euro currency

Up until now, we have developed banking machines for various kinds of paper currency using neural networks. In this paper, we report an enhanced neuro-recognition system to increase the number of recognition patterns using axis-symmetrical mask and two image sensors. One sensors purpose is discrimination for a known image and another one is exclusion for an unknown image. Concretely, we implement the proposed method to an experimental system, which has two sensors, arranged one above and one below the moving banknote. Finally, we apply this proposed method to Euro currency, which will be issued in 2002, using dummy notes. The effectiveness of the proposed method is shown, numerically.

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.

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.

Neural Network Recognition System Tuned by GA and Design of Its Hardware by DSP

Abstract We have proposed paper currency recognition system by a neural network (NN) to develop new types of the paper currency recognition machines. This system is constructed by some core techniques. One is the small scale neuro-recognition technique using masks. The one is the mask optimization technique using a genetic algorithm (GA). The last is the neurohardware technique using a digital signal processor (DSP). In this paper, in order to determine the excellent masks which can generate the characteristic values of the input image effectively, we adopt the GA to the mask optimization and tune the neurorecognition system. We show the effectiveness of this technique using the Italy currency. Still more, we design some high speed neuro-recognition machines. Its recognition speed is ten times faster than current currency recognition machines. Finally, the feasibility and effectiveness of the neuro-recognition system is shown by using world wide currency.

Development of autonomic neural board for banknotes and advancement to palm prints recognition

The authors (1996, 1998) have previously proposed a banknote recognition system using a neural network to develop new types of banking machines such as banknote readers and sorters. In this neural recognition system, the banknotes data have to be transported from the banking machines to a personal computer (PC) for learning. After learning on the PC, the neural parameters such as weights and others are then downloaded to the neural board. However, one has to cope with the recovery of the recognition ability for various fluctuations of the banknotes in the field. In this paper, we implement the neural learning algorithm to this board enabling it to execute learning by itself and extend its specification to realize the intelligent machines. We construct the experimental system. Then we show the effectiveness and possibility of robust autonomic learning using banknotes and palm prints data.

Development of autonomic neural board and advancement to palm prints recognition

Previous work by the authors has proposed a banknote recognition system using a neural network (NN) to develop new types of banking machines such as banknote readers and sorters. The feasibility and effectiveness of our research in the commercial market has been shown clearly. We implement the neural learning algorithm to a neural board and extend its specification in order to realize intelligent machines, which can be tuned by itself in the market. Furthermore, we advance this neural recognition system to palm prints recognition. We construct an experimental system for palm prints recognition using the proposed neural board, image scanner and PC. Finally, we show the possibility of individual recognition using palm prints, experimentally.