To address the issue of existing circular meter reading algorithms being susceptible to adverse factors such as shooting angles and complex environments, this paper proposes a perspective rectification-based automatic circular meter reading system successfully deployed on mobile devices. Initially, a lightweight meter detection model and key target detection model based on YOLOv8n were redesigned. After simplifying the network structure, a mobile-optimized lightweight model was developed by integrating PConv into the LiteFFM module and employing LAMP pruning techniques. The improved model significantly reduces computational requirements, with a parameter reduction of 97.75% and GFLOPs as low as 0.4. Furthermore, the paper introduces an effective circular meter rectification method that constructs a rectification matrix from edge contour point sets of the meter to eliminate distortions caused by shooting tilt, integrating markers and scale characters for precise rotational rectification. Lastly, an enhanced angle method calculates accurate readings, and a rectification guidance mechanism within the app optimizes the user shooting experience. Experiments show that under severely tilted conditions, the correction algorithm can reduce the average relative reading error by 60.68%. The system runs at 9 FPS on mobile phones, with an average relative reading error of only 1.76% in complex environments, outperforming existing advanced methods and demonstrating high robustness.