| 王浩楠,张晓青,郭阳宽,李文庆.基于机器视觉的轮胎胶料表面字符识别[J].电子测量与仪器学报,2021,35(1):191-199 |
| 基于机器视觉的轮胎胶料表面字符识别 |
| Recognition of characters on tire rubber surface based on machine vision |
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| DOI： |
| 中文关键词: 轮胎胶料 点阵字符 字符分割 字符识别 |
| 英文关键词:tire rubber dot matrix characters character segmentation character recognition |
| 基金项目:国家自然科学基金（61801247）、江苏省自然科学基金（BK20180945）项目资助 |
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| Author | Institution |
| Wang Haonan | School of Instrument Science and Opto Electronics Engineering, Beijing Information Science and Technology University,Beijing 100192, China |
| Zhang Xiaoqing | School of Instrument Science and Opto Electronics Engineering, Beijing Information Science and Technology University,Beijing 100192, China |
| Guo Yangkuan | School of Instrument Science and Opto Electronics Engineering, Beijing Information Science and Technology University,Beijing 100192, China |
| Li Wenqing | Beijing Wohua Attraction SciTech Co.,Ltd, Beijing 100085, China |
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| 摘要点击次数: 1384 |
| 全文下载次数: 3 |
| 中文摘要: |
| 针对目前轮胎胶料表面的5×7点阵喷码字符人工识别存在劳动强度大、效率低、智能化水平低等问题，提出了一种基于机器视觉的轮胎胶料表面字符识别方法，首先利用字符出现区域较为稳定且字符为黑底白字进行快速定位与背景分割、形态学操作消除干扰；然后创新性地运用简化垂直投影法配合字符宽度的阈值限制，并对“1”和“I”与其他字符宽度不同的特殊情况进行处理，实现字符分割；最后，采用基于标准相关匹配的模板匹配方法实现字符识别。实验结果表明，所提方法的字符识别准确率达到9951%，达到了预期的识别效果。 |
| 英文摘要: |
| At present, the 5×7 dot matrix spray code character recognition on the surface of tire rubber has many problems, such as high labor intensity, low efficiency and low intelligence level. Based on machine vision, a novel character recognition method for tire rubber surface is proposed. Firstly, the character occurrence area is stable and the character is white on black background for fast positioning, background segmentation and using morphological manipulation to remove interference. Then, the simplified vertical projection method is innovatively used to fit the threshold of character width, and the special cases of “1“and “I” which are different from other character widths are processed to achieve character segmentation. Finally, the template matching method based on standard correlation matching is used to realize character recognition. Experimental results show that the accuracy of the proposed method is 9951%, which achieves the expected recognition effect. |
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