基于机器视觉的汽车电器箱部件字符识别

Liuzhen Zhang, Dongdong Pang, Pengge Ma
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引用次数: 2

摘要

根据电箱部件的特点,采用一般光学字符识别方法对部件上的字符进行识别。研究了识别过程中的多分类问题。首先,介绍了基于自动检测的汽车电箱系统的机器视觉组成部分;然后,根据多分类支持向量机学习,提出了一种支持向量机与主动学习相结合的思想模型,并根据学习过程中的模糊样本建立了查询机制;最后,提出了一种基于询问策略的多分类器构建算法。通过软件编程将该算法应用于新能源电气箱体部件的字符识别,验证了算法的有效性,增强了算法的鲁棒性,提高了识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Character recognition for automotive electrical box components based on Machine vision
According to the features of the electric box components, the characters on the components are identified from the general optical character recognition process. The problem of multi classification in the recognition process is studied. First, the paper introduces the machine vision components of automotive electrical box system based on automatic detection; then, according to the multi classification support vector machine learning, we propose a support vector machine and active learning and give the idea of combining active learning model, the inquiry mechanism is established according to the fuzzy sample in the learning process; Finally, a multi-classifier construction algorithm is given based on the interrogation strategy. The algorithm is applied to character recognition of new energy electrical box components by software programming, and the effectiveness of the algorithm is verified, the robustness of the algorithm is enhanced, and the recognition rate is improved.
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