基于投影阈值分割和数字序列校正的高噪声数字仪表图像识别方法

High Noise Digital Instrument Image Recognition Method Based on Projection Threshold Segmentation and Number Sequence Correction

针对数字仪表图像噪声大、图像特征信息不足导致图像识别准确率低的问题,提出了一种基于卷积递归神经网络结合投影阈值分割和数字序列校正的高噪数字仪表图像识别方法。首先,用投影阈值分割二值化算法对图像进行预处理:使用垂直投影法将图像划分为不同区域,根据不同区域的噪声强度自适应设定二值化阈值,对图像进行二值化处理,降低噪声;其次,根据图像之间数字规律变化特点,利用数字序列校正算法将单个数字识别转换为数字序列识别,通过对比不同数字序列的识别概率得出识别结果,解决单张图像特征信息不足导致识别准确率低等问题。实验结果表明,在高噪声数据集上,相较于卷积递归神经网络模型,提出的高噪声数字仪表识别模型在准确率方面提高了约61.95%,达到93.58%。

To solve the problem of low recognition accuracy due to high noise and insufficient feature information of digital instrument images,this paper proposes an image recognition method of high noise digital instrument based on convolution recursive neural network combining projection threshold segmentation and number sequence correction.Firstly,the projection threshold segmentation binarization algorithm is proposed to preprocess the image.The vertical projection method is used to divide the image into different regions,and the binarization threshold is set adaptively according to the noise intensity of different regions to binarization the image and reduce the noise.Secondly,according to the changing characteristics of the number rules between images,the number sequence correction algorithm is used to transform a single number recognition into a number sequence recognition,and the recognition result is obtained by comparing the recognition probability of different number sequences,so as to solve the problem of low recognition accuracy caused by insufficient feature information of a single image.The experimental results show that,compared with the convolutional recursive neural network model,the accuracy of the high-noise digital instrument recognition model proposed in this paper is improved on the high-noise data set by about 61.95%,reaching 93.58%.