【摘要】 针对 UK心理测试自动分析系统的手写体数字识别问题 ,提出了结构特征和统计特征相组合的三级分类方案 .经过印刷体去除、二值化、作业量判别等预处理之后 ,一级分类器提取点、线、圆等结构特征并进行组合构造相应模板 ,然后采用粗细两阶段方案进行模板匹配 ;二级分类器提取区域模糊统计特征 ,构造了 10个一对多的SVM分类器 ;三级分类器提取投影特征、笔划特征、Fourier变换特征等 ,然后利用 RBF神经网络进行分类 .实验表明该方法合理有效 .更多还原

【Abstract】 A three-stage classification system of handwritten digits recognition is presented for the automatic analysis system in UK psychology test. After eliminating the printed digits, binarization and thinning, some structural features, including the points, lines and circles are extracted for the first-stage classifier. In this stage, two steps are taken, viz. the coarse and the fine classification. Zoning statistical features and 10 one-versus the rest support vector machines are used in the second-stage classifier. RBF network is used as the third-stage classifier, and the features extracted are stroke features, projection features and Fourier transform features. Experiments have shown the effectiveness of the method.更多还原