纸币冠字号预处理及组合特征识别方法

摘要

针对纸币冠字号识别中传统方法速度慢和准确率低的问题，改进预处理的关键算法，提出一种组合特征识别方法。通过改进图像定位、旋转校正、二值化和去噪滤波等预处理算法，提高处理速度和二值图像质量；在此基础上，提出一种结合必选特征和可选特征的组合特征提取方案，采用多叉树分类器设计组合特征识别算法。实验结果表明，与传统的定位、旋转、二值化和字符识别算法相比，该方法具有更高的识别率和处理速度。%To solve the problem of low accuracy and low speed of the traditional methods in paper currency number recognition, the key algorithms of preprocessing were improved and a method of combined-feature recognition was proposed.The processing speed and the quality of binary image were improved by modifying the preprocessing algorithms,including the image location,the rotation,the binarization and the filtering.Based on these,a combined-feature extraction solution combining the required fea-tures and optional features was proposed,and a combined-feature recognition algorithm using multi-tree classifier was designed. Experimental results reveal that,compared with the traditional location algorithm,rotation algorithm,binarization algorithm and character recognition algorithm,the presented method has higher recognition accuracy and processing speed.