## 1 Introduction  
Industry 4.0 is considered the latest stage of industrial development. It is based on information technology to promote the intelligent development of the industrial field, emphasizing the intelligent upgrading of manufacturing through technologies such as the Internet of Things, big data, and artificial intelligence. All manufacturing processes will be digitized within this framework, enabling highly individual production. Among this, the intelligent manufacturing pursued by Industry 4.0 requires many readings of various meters, which are critical for monitoring production processes, optimizing manufacturing processes, improving product quality, and more. In the early stages of industrial development, meter readings mainly relied on manual operation and management, and manual meter readings came into being at this stage. Traditional industrial mechanical meters are still widely used in the transition process of industrial development, necessitating manual meter reading. While technologies offer alternative methods of meter readings, such as Bluetooth, LTE, and LoRa, these technologies require additional hardware to be installed on mechanical meters, which may not always be feasible or cost-effective, and replacing all mechanical instrumentation requires a significant investment of capital and technical support. Comparatively, image-based meter reading requires only a camera, which is already widely used in many devices, providing a more comprehensive solution that can handle multiple types of meters and reading scenarios. Therefore, meter reading recognition technology can be used as a critical technical means to achieve the goals of Industry 4.0, helping enterprises achieve automation, digitization, and intelligence. The initial research focused on theoretical recognition studies, later expanding to recognizing simple characters such as 0-9 numerals and Chinese symbols. The OCR technology can be classified into two categories: traditional and deep-learning-based character recognition methods. Traditional OCR technology typically comprises three sequential stages: preprocessing, text area localization, and text recognition. Notably, the classifiers utilized in the traditional recognition phase encompass techniques such as template matching, line cross, and support vector machines (SVMs). [PERSON] et al. [1] began to use OCR technology for digital meter recognition earlier, proposed a method for digital meter recognition based on fuzzy theory, and constructed a digital recognizer that can quickly recognize digital displays. However, the quality requirements of the collected pictures are relatively high, and it needs a better segmentation effect for oblique images, which may cause digital incompleteness and affect the recognition effect. Traditional template matching methods [2; 3; 4] find it difficult to distinguish slightly deformed, displaced, and rotated images due to interference and have poor recognition effects for digital characters with different fonts and inclinations. [PERSON] et al. [5] proposed an improved standard-template-matching method that subdivides the various interferences of each type of number and makes multiple templates for matching, reducing the recognition error caused by interference to a certain extent. However, as the number of templates produced increases, the algorithm's complexity will also increase, so this improvement scheme has specific requirements for computer performance. [PERSON] et al. [6] proposed the line cross method, a deterministic recognition algorithm. Although this method has high accuracy, adapting to complex environmental scenarios takes time and effort. [PERSON] et al. [7] and [PERSON] et al. [8] proposed a digital meter recognition method. This method uses a linear support vector machine based on the histogram of the oriented gradient (HOG) feature, which uses HOG feature detection and performs digital recognition through SVM classification. [PERSON] and [PERSON] [9] initially used the HOG feature descriptor to extract features from character images.