Abstract: Leveraging electric vision imaging technology to identify instrument readings in substations offers substantial benefits in real-time equipment monitoring and elevating operational and maintenance intelligence. However, many existing instrument image recognition solutions for substations rely on pointer deflection angles, despite the need for enhanced precision and robustness. These methods overlook critical data, such as device status, numerical intervals reflected by dial color, and dial character information. This paper introduces a novel method for automatic recognition of instrument readings and dial information in substations. First, it proposes a dial type position detection algorithm based on YOLO-E, achieving image calibration through perspective transformation. Second, building upon OCRNet’s object region context extraction structure, it incorporates a parallel branch with polarized self-attention to rationally utilize channel feature maps with varying weights. This results in a dial segmentation algorithm based on an improved OCRNet. By segmenting scales, pointers, and color bands, this method achieves precise segmentation and identification of meter readings and crucial additional information. Finally, using PGNet, the method recognizes dial information, enabling automatic acquisition of data like meter range parameters and readings for multi-range dials. A case study demonstrates that, compared to other advanced electric vision algorithms, the proposed method not only enhances reading recognition accuracy but also effectively detects and extracts additional dial information. This advancement supports the digital transformation of operations and maintenance.