关键词: 光学字符识别, 文本检测, 文本识别, 深度学习

Abstract: Label detection of electrical cabinets in the distribution room of chemical plants is an important part of intelligent and autonomous inspection. The traditional manual detection method has the disadvantages of low detection efficiency, high rate of missed detection and high false detection rate, and risky for workers. The OCR-based deep learning model can efficiently detect the position of the text box of the label on the electrical cabinet and accurately identify the text content. On the self-built electrical cabinet dataset, a text detection model DBNet and a text recognition model ABINet are trained to detect the position of the electrical cabinet text box. The accuracy of the DBNet model in detecting text boxes on the test set can reach 96%, the accuracy of ABINet in recognizing text can reach 89%, and the average detection speed of the model can reach 1 image per second under the RTX2080Ti graphics card. The methods applied in the experiment meet the requirements of industrial detection accuracy and detection speed.

Key words: Optical Character Recognition, Text Detection, Text Recognition, Deep Learning