基于图像阈值优化及改进SVM的电表数字识别

Digital Identification of Electric Meter Based on Image Threshold Optimization and Improved SVM

针对电表出厂的校对和极端环境的测试工作,仍然需要人工检测电表是否出现内部元件错误或误差问题,提出一种基于图像阈值优化及改进SVM的电表数字识别方法。首先对图像使用边缘查找获取图像的显示区域,采用自适应阈值进行二值化处理,再对图像进行一系列的滤波处理,然后为进一步提取单个数字的图像,结合图像阈值优化,在保留数字图像特征值的前提下,去除多余的特征值,将显示区域图像划分成若干个单个数字图像。最后基于改进的SVM多分类识别模型进行0~9每个数字的训练,使用训练后的模型依次对单个数字图像进行识别。实验结果表明,对比经典的卷积神经网络模型对LED液晶数字的识别,基于图像阈值优化及改进SVM模型有着更快的识别速度和较高的准确率。

Aiming at the calibration of the electric meter and the test work in extreme environments,it is still necessary to manu⁃ally detect whether the electric meter has internal component fault or errors.A research method of electric meter digital recogni⁃tion based on image threshold optimization and improved SVM is proposed.First,we use edge search to obtain the display area of the image,use adaptive threshold for binarization,and then perform a series of filtering processing on the image,and then fur⁃ther extract the image of a single number,combined with image threshold optimization,before retaining the digital image.On the premise of eigenvalues,the redundant eigenvalues are removed,and the display area image is divided into several single digital images.Finally,based on the improved SVM multi-class recognition model,each digit from 0 to 9 is trained,and the trained model is used to identify the single digit image in turn.The experimental results show that compared with the classical convolu⁃tional neural network model for the recognition of LED liquid crystal digits,the optimization and improvement of the SVM model based on the image threshold have faster recognition speed and higher accuracy.