Meters can accurately reflect the operation status of substation electrical equipment, to overcome the manual inspection caused by meter leakage and misreading and so on, put forward a PAT-Unet (Pyramid split attention And Transformer Unet,PAT-Unet) neural network based on the number of intelligent reading algorithms for the leakage current meter display intelligent reading. Firstly, the PAT-Unet neural network is designed to segment the pointer and dense scale of the meter. The feature aggregation module and residual feature dispersion module are constructed in the coding layer of the network to enhance the feature extraction ability. Design the Transformer feature concentration module for deep semantic information fusion to enhance the segmentation accuracy of acceptable targets; introduce the pyramid slicing attention mechanism to strengthen the information interaction between the network coding and decoding layers. The information interaction between the coding layer and the decoding layer of the network is enhanced. Ability. Combine the contour detection algorithm and the minimum outer rectangle algorithm to calculate the key point of the scale segmentation results, and use perspective transformation to complete the correction of tilted meters; then use the K-means clustering algorithm to locate the center of the leakage current meter; finally, according to the center of the metering circle, use the polar coordinate transformation to expand the sector dial into a rectangle, and get the number of the leakage current meter by calculating the distance between the zero scale, the pointer and the maximal scale respectively. The leakage current meter is obtained by calculating the distance relationship between the zero scale and pointer and the maximum scale respectively. Experiments have demonstrated that the proposed algorithm can correct the tilted dashboard and provide intelligent readings of the leakage current meter representation while ensuring the accuracy of the readings.