Text Recognition for Speed Controlling in Vehicle

This project presents an intelligent vehicle speed control system based on real-time text recognition using computer vision and embedded systems. The system utilizes a webcam to capture road images, and Python-based image processing with OpenCV and Tesseract OCR is employed to detect and recognize speed limit signs. The extracted speed information is transmitted to an Arduino Uno, which dynamically controls the vehicle’s speed through a motor driver using PWM signals. An ultrasonic sensor is integrated for obstacle detection to ensure collision avoidance by automatically reducing or stopping the vehicle when objects are detected within a threshold distance. Additionally, a GPS module is used to monitor real-time speed and location, enabling comparison between actual speed and detected limits. A buzzer alert system is implemented to notify the driver during overspeed or unsafe conditions. The system also includes an LCD display for real-time visualization of speed limits and system status. This integrated approach enhances road safety by combining text recognition, sensor fusion, and automated speed regulation, making it suitable for advanced driver assistance systems and smart transportation applications.