Abstract: A banknote classification system is designed and implemented for US dollar system.The classification system can process reliably the US dollars in high speed.Banknote sorter is one kind of the automatic financial tool.Banknote image analysis technique is hard-core of the sorter.It is a complex photic & mechatronic system.Its hardware is composed of image sensor，signal capture controller and digital signal processor.After capturing the banknote image with high speed，the DSP completes the image location，declining adjusting，feature extraction and image analysis.The banknote classification is finished by BP neural networks.The system obtains good results in the practical application.It can processes the banknote at speed of 800/min without misclassification.

Key words: Digital Singnal Processor（DSP）, image processing, feature extraction, neural networks

摘要： 提出了在纸币高速运行条件下对纸币图像进行快速可靠的识别和系统实现方法。清分机是一种自动纸币整理机具。纸币图像分析技术是清分机的核心技术。该系统是光机电一体化的设备，其硬件部分包括线阵图像传感器、信号采集控制器、数字信号处理器。硬件平台捕获高速运行的美元图像后，由信号处理算法完成美元图像的定位、几何校正、特征提取以及图像分析运算，使用BP神经网络完成图像识别。该系统在实际应用中取得了良好的效果。美元处理系统的速度可达800张/min，拒识率小于0.01%。

关键词: 数字信号处理, 图像处理, 特征提取, 人工神经网络