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计算机系统应用:2020,29(3):278-283
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基于机器视觉的轴承压印字符识别
(杭州师范大学 杭州国际服务工程学院, 杭州 311121)
Bearing Imprint Character Recognition Based on Machine Vision
(Hangzhou Institute of Service Engineering, Hangzhou Normal University, Hangzhou 311121, China)
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投稿时间:2019-08-05    修订日期:2019-09-03
中文摘要: 目前许多轴承生产线上利用人工肉眼识别轴承工件号,这样不仅识别效果不好且效率低.在本文设计了一种基于机器视觉的轴承压印字符识别算法,该算法有利于轴承的生产以及后续的管理工作.首先对采集到的图像进行高斯滤波降噪,减少噪声对后续操作的影响;然后利用最小二乘法对ROI圆环进行提取,确定要进行操作的图像区域;接着使用八分之一圆扫描方法将圆环图像展开,使得字符识别操作更加简洁;随后对字符进行切分、归一化;最后使用SVM对字符进行识别.实验表明,该方法能够实现轴承压印字符识别,识别准确率在98%以上,并且具有良好的鲁棒性,系统响应速度快,能够满足工业需求.
Abstract:At present, many bearing production lines use manual naked eyes to identify the bearing workpiece number, which not only has poor recognition effect but also has low efficiency. In this study, a bearing embossing character recognition algorithm based on machine vision is designed, which is beneficial to bearing production and subsequent management. Firstly, the noise of the collected image is reduced by Gaussian filtering to reduce the influence of noise on the subsequent operation, and then the least square method is used to extract the ROI ring to determine the image region to be operated. Then the 1/8 circle scanning method is used to expand the ring image to make the character recognition operation more concise; then the character is segmented and normalized; finally, the character is recognized by SVM. The experimental results show that this method can realize bearing imprint character recognition, the recognition accuracy is more than 98%, and has good robustness, the system response speed is fast, and can meet the needs of industry.
文章编号:7306     中图分类号:    文献标志码:
基金项目:国家自然科学基金(11772301);浙江省自然科学基金(LY17F0220016);杭州师范大学第二轮专业学位研究生生课程教学案例库建设,工业智能制造项目(横向)
引用文本:
张桢铖,周迪斌,朱江萍.基于机器视觉的轴承压印字符识别.计算机系统应用,2020,29(3):278-283
ZHANG Zhen-Cheng,ZHOU Di-Bin,ZHU Jiang-Ping.Bearing Imprint Character Recognition Based on Machine Vision.COMPUTER SYSTEMS APPLICATIONS,2020,29(3):278-283

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