Egyptian Car Plate Recognition System

-License plate recognition is an important technology with many applications like electronic toll collection, traffic law enforcement, and vehicle tracking and identification. The goal of an LPR system is to automatically detect and extract the license plate number from an image or video of a car -License plate detection. YOLOv8 can be trained on a dataset of car images with labeled license plates to build a detection model. At runtime, it will take an input image, run the model, and return the coordinates of any license plates it finds -The license plate image can be extracted from the original image using the coordinates identified by YOLOv8. OpenCV functions like cropping and perspective transforms can prepare the license plate image for the next step -The license plate characters then need to be segmented into individual letters/digits. This involves techniques like morphological operations, contours, and character segmentation in OpenCV -Optical character recognition is used to identify each character. The segmented characters can be fed into a pretrained OCR model like Tesseract or a custom CNN model built with OpenCV and machine learning -The pipeline outputs the license plate text which can then be used for various applications. Building this system with OpenCV and YOLOv8 allows for fast, accurate, and robust license plate recognition in real-time. It has important uses in transportation, law enforcement, and security domains