针对指针式电力仪表数据识别中图像字符粘连、背景干扰强及识别歧义等问题，提出了一种图像预处理与深度识别网络协同的架构，结合字符检测模块与语义理解模块构建了面向复杂场景的指针式电力仪表数据统一识别方法。该方法利用利用Python中提供的Paddle框架构成光学字符识别（OCR）算法对图像中电力仪表的量程进行自动获取，然后基于U²-Net网络模型对电力仪表表盘中的指针、刻度线进行识别，利用Hough线变换和圆心拟合的方法确定指针旋转中心并将获取到位置信息的掩膜图像进行图像变换为矩形图像，随后竖向叠加生成一维数组，最后通过两种数据对比方式获取指针在刻度线上的相对位置，完成电力仪表的数据读取。在真实场景下进行实验验证，结果表明所提出的方法在多个电力计量仪表类型上识别准确率均能达到99%，相较于单一使用图像处理、深度识别网络处理方法在复杂场景下的适应性更强，识别误差显著降低。

To address the challenges of character adhesion, strong background interference, and recognition ambiguity in pointer-type power metering instrument data identification, this study proposes a unified framework that synergizes image preprocessing with deep recognition networks. By integrating a character detection module and a semantic comprehension module, we develop a robust method tailored for complex scenarios. The approach employs the Paddle-based optical character recognition (OCR) algorithm to automatically acquire the measurement range of power meters from images. Leveraging the U²-Net network model, it precisely segments the pointer and scale lines on the meter dial. The Hough line transform and circle center fitting methods are utilized to determine the rotational center of the pointer. The positional mask images are then transformed into rectangular coordinates, vertically stacked to form a 1D array, and analyzed through dual data-comparison strategies to calculate the relative position of the pointer on the scale lines, thereby completing meter data extraction. Experimental validations under real-world conditions demonstrate that the proposed method achieves an average recognition accuracy of 99% across multiple types of power metering. Compared to deep learning network approaches, it exhibits superior adaptability in complex environments, with a significant reduction in recognition errors.