基于SVM分类器的集装箱箱号识别法

摘要

为准确、高效地识别集装箱箱号,提出基于支持向量机(Support Vector Machine,SVM)分类器的集装箱箱号识别法.在对大量箱号图片进行实验并统计各种特征识别率的基础上,经过预处理、箱号定位、字符分割,得到36×22像素大小的二值化图像;提取箱号字符的边界和质心特征、改进的灰度直方图特征以及边缘方向直方图特征,将这些特征进行组合形成特征向量并进行降维和归一化处理;利用SVM分类器把处理过的特征向量进行分类并识别.实验结果表明,该方法平均识别正确率可以达到95%,高于使用单一特征的识别、简单的模板匹配算法以及特征加权(八邻域)模板匹配算法.%In order to recognize container code precisely and effectively, a recognition method of container code based on support vector machine (SVM) classifier is proposed. On the basis of experiment by using many container code images and count recognition rate of various features, binary images with size of 36 × 22 pixels are obtained by preprocessing, container code locating and character segmentation. The character features including edge, center of mass, improved grayscale histogram and edge direction histogram are extracted and combined into feature vector which is processed by dimensionality reduction and normalization. The processed feature vector is classified and recognized through SVM classifier. Experiment results show that the average recognition rate can reach 95％, which is higher than that of single feature recognition, simple template matching algorithm and weighted feature (8-neighborhood) template matching algorithm.