## 1 Introduction
Then, they used the fuzzy-based multi-kernel spherical support vector machine (FMS-SVM) technique to classify the extracted features. The SVM-based method has better accuracy and better robustness. Still, it is unsuitable for processing extensive sample data, and it is not easy to find the appropriate hyperparameters of the SVM classifier and the best features to be extracted. Although traditional OCR technologies have established a well-developed technical process system, they demand a complex and time-consuming preprocessing phase. Generally, it necessitates images with a relatively uncluttered background; even a slightly intricate picture may cause recognition failure. Furthermore, limitations, such as the inadequate recognition of digital characters with varied fonts and inclinations, result in poor adaptability, a low fault tolerance, and an inability to recognize text in diverse layouts, complex backgrounds, and low-resolution environments. With the advent of deep learning, a plethora of object-detection networks have emerged; notable among them are RCNN (Region-CNN) series networks [10; 11; 12; 13], YOLO (you only look once) series networks [14; 15; 16; 17; 18], and SSD networks [19]. Many scholars have utilized these vision-based target detection networks for OCR character recognition technology. [PERSON] et al. [20] introduced an end-to-end digital meter recognition method without segmentation for readings. The author uses the Caffe framework to train a convolutional neural network to read directly from the input image without detecting the meter reading area. However, the author's method was only evaluated on a large private dataset with close to 180,000 training samples, among which not only are most of the images centered on the digital meter, but also numbers occupy most of the images. Therefore, as the author pointed out, this system can only be used when the meter is in the center of the test image or the meter needs to be bigger in the test image. [PERSON] et al. [21] used a fully convolutional neural network to realize the recognition of digital meter representation numbers and obtained the final result through graph-to-graph prediction. [PERSON] et al. [22] proposed a variable convolutional network structure, optimized the receptive field of the convolutional neural network, and used this neural network for the character segmentation and recognition of digital meters. [PERSON] et al. [23] proposed a two-stage digital meter detection and recognition model based on Faster R-CNN, which enables the model to detect meter numbers under different lighting conditions more accurately. [PERSON] et al. [24] designed a two-stage method for a digital meter detection and recognition model. This method compares three CNN-based models in terms of their dataset: CR-NET, Multi TaskLearning, and CRNN, and finally combines Fast-YOLO and CR-NET to achieve the best meter area positioning and reading recognition effect on its meter dataset. [PERSON] et al. [25] used the improved SSD network to detect the meter image. They changed the basic SSD network from VGG-16 to ResNet50, aiming to extract more feature information in the picture to ensure speed and improve recognition accuracy. [PERSON] et al. [26] introduce an improved capsule network that overcomes the limitation of the basic capsule network [27], which is challenging to execute on devices with limited computational resources. Additionally, they integrate morphology-based data preprocessing with the network to improve meter reading recognition. [PERSON] et al. [28] suggest a method for localizing and recognizing cubic meter and liter areas on water meters using YOLOv5 by annotating the dataset. [PERSON] et al. [29] utilized fast Fourier transform (FFT) for blur detection and DeblurGANv2 for recovering the blurred image. Additionally, They employed Polygon-YOLOv5 to detect the reading area and CRNN for recognizing the readings. Although they claim that their method was applied to inspection robots, it does not ensure real-time performance, which is crucial for robots.