| UK心理测试自动分析系统的手写体数字识别 |
| |
| 引用本文： | 柳回春,马树元,吴平东,杨峰,曾兴生,毕路拯.UK心理测试自动分析系统的手写体数字识别[J].北京理工大学学报,2002,22(5):599-603. |
| |
| 作者姓名： | 柳回春 马树元 吴平东 杨峰 曾兴生 毕路拯 |
| |
| 作者单位： | 北京理工大学,机械工程与自动化学院,北京,100081 |
| |
| 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.
|
| 关 键 词： | 手写体数字识别 统计特征 结构特征 支持向量机 RBF |
| 文章编号： | 1001-0645(2002)05-0599-05 |
| 收稿时间： | 2001/11/5 0:00:00 |
| 修稿时间： | 2001年11月5日 |
Handwritten Digits Recognition for Automatic Analysis System of UK Psychology Test |
| |
| LIU Hui-chun,MA Shu-yuan,WU Ping-dong,YANG Feng,ZENG Xing-sheng and BI Lu-zheng.Handwritten Digits Recognition for Automatic Analysis System of UK Psychology Test[J].Journal of Beijing Institute of Technology(Natural Science Edition),2002,22(5):599-603. |
| |
| Authors: | LIU Hui-chun MA Shu-yuan WU Ping-dong YANG Feng ZENG Xing-sheng and BI Lu-zheng |
| |
| Institution: | School of Mechanical Engineering and Automation, Beijing Institute of Technology, Beijing100081, China;School of Mechanical Engineering and Automation, Beijing Institute of Technology, Beijing100081, China;School of Mechanical Engineering and Automation, Beijing Institute of Technology, Beijing100081, China;School of Mechanical Engineering and Automation, Beijing Institute of Technology, Beijing100081, China;School of Mechanical Engineering and Automation, Beijing Institute of Technology, Beijing100081, China;School of Mechanical Engineering and Automation, Beijing Institute of Technology, Beijing100081, China |
| |
| 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. |
| |
| Keywords: | handwritten digits recognition statistical feature structural feature support vector machine RBF |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《北京理工大学学报》浏览原始摘要信息 |
| 点击此处可从《北京理工大学学报》下载免费的PDF全文 |
|
