Multi-object Paper Money Recognition Technology Based on AlexNet Model
Haiyue Bi, Zhiqiang Tian, Tong Ye, Jingxiong Yue · 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC) · 2021
The use of physical currency is currently the most widely used method of transaction. The pattern recognition technology of paper currency has a wide range of applications, such as self-checkout machines, ATMs, vending machines, and devices for the blind. In modern society, there are more and more convenience stores. If they can have the RMB recognition system, they will save energy from cashiering. That is the reason for choosing this research Direction. However, it fails to identify images rotated over a certain angle or with excessive noise at first, and it cannot be 100% exact that the accuracy of our work now. But it will be perfected. To realize the function, this paper proposed a neural network model, which can accurately identify RMB. The best performance of the image process can be achieved by utilizing the Automatic white balance, the Automatic brightness, and the Automatic de-noising. Then it is time to cut and extract the image. At last, the image is put into the neural network model for recognition. The defect of this study is that it ignores the influence of possible fluctuation of image quality on the recognition results in the real situation. The related work established a Radial Basis Function Network based on notes interesting features and correlations between images. The image processing technology is used to improve image quality. Through the above methods, the work can identify all kinds of RMB. Moreover, it can already ensure that the success rate of RMB identification can reach more than 90%.