摘要
以手写体数字的识别为研究中心,研究了针对手写体数字的特征提取和特征识别。利用像素点统计特征,图像结构交截特征,小波分析特征提取出了多维特征。介绍了级联分类的思想,引入拒识率的概念。提出了基于改进BP神经网络和支持向量机级联的手写体数字识别系统。实验验证,该方法能有效提高字符分类的识别率。
Taking handwritten numeral recognition as the research center, the feature extraction and feature recognition of handwritten numerals are studied. Based on the statistical characteristics of pixels, the cross section characteristics of image structure, the feature of wavelet analysis the feature is extracted. This paper introduces the idea of cascade classification, and introduces the concept of rejection rate. A handwritten numeral recognition system based on improved BP neural network and support vector machine cascade classifiers is proposed. Experimental results show that the proposed method can effectively improve the recognition rate of the character classification.
出处
《电脑知识与技术(过刊)》
2016年第7X期180-182,共3页
Computer Knowledge and Technology
基金
面向工业大型机械智能网络控制的多信息视觉识别和三维重构关键技术研究(编号:14JC1402203)
国家科技支撑计划:电子器件组装高速精密机器人与自动化生产线研发及产业化
子课题名称:基于机器视觉的电子器件定位研究(编号:2015BAF10B01-01)
关键词
特征提取
小波分析
神经网络
支持向量机
级联分类
feature extraction
wavelet analysis
neural network
support vector machine
cascade classify