This mini-project demonstrates a number plate detection system using OpenCV and parallel processing techniques. The system captures video frames, detects number plates, and processes the images in parallel to improve performance.
In this project, parallel processing is utilized to enhance the performance of num ber plate detection. The Python multiprocessing module is used to achieve this. The main idea is to separate the tasks of frame capturing and plate detec tion into different processes that run concurrently.
In a sequential processing approach, the main program captures a frame from the video stream, processes it to detect number plates, and then displays the result. This sequence of operations can create a bottleneck, especially when real-time performance is required. By using parallel processing, we can overlap the operations of capturing frames and detecting plates, thus improving the throughput and responsiveness of the system
This project demonstrates the implementation of a number plate detection sys tem using parallel processing to improve efficiency. By leveraging OpenCV for image processing and EasyOCR for text recognition, the system can effectively detect and recognize number plates in real-time. The parallel processing ap proach significantly enhances performance compared to a sequential method, making it suitable for applications requiring high throughput and low latency