In harsh environments such as high temperature, high pressure, and strong magnetic fields, pointer instruments have shown superior performance compared to digital instruments. Therefore, the research on pointer instrument reading recognition has significant practical significance. In recent years, the combination of deep learning and computer vision has become a key focus in the research of pointer instrument reading recognition technology. This paper first outlines the process of pointer instrument reading recognition, and then discusses the research status and progress of pointer instrument reading recognition technology from three aspects: image preprocessing, dial region detection, and reading recognition. The paper elaborates on both traditional machine learning methods and deep learning methods. Finally, it introduces publicly available pointer instrument datasets and application scenarios, and provides prospects and suggestions for future research from the aspects of deep learning algorithms, inspection robot characteristics, and the establishment of public datasets.