Language shadow- Based on An Improved Spatiotemporal Network Model Lip Recognition System

Lip recognition technology is of great significance, but the traditional technology has limitations such as low accuracy, poor real-time and poor visualization. This study developed a lip recognition system based on an improved spatiotemporal network model. Fusion YOLOv7, algorithm, improved TSM-ResNet50 model and Flask framework construction innovative scheme: using YOLOv7 to detect lip position accurately; improved TSM-ResNet50 combines multiple modules and mechanisms to improve the dynamic recognition ability of lip; Flask framework for visualization and interaction. The experiment shows that the system achieves high accuracy and real-time lip recognition, can accurately locate lip position, identify action and visual display, and supports offline and online real-time video recognition. It has broad application prospects in special education, public security, and other fields and has social and commercial value.At the same time, the project details the marketing strategy, business model, financial analysis, and various risk control measures. The comprehensive implementation is expected to promote the development and application of lip recognition technology, bringing a positive impact.