The project aimed to develop a system capable of automatically identifying vehicles and their license plates using advanced machine learning techniques. The core of the system is based on the YoloV5 neural network, which is specifically trained to detect objects in images—in this case, vehicles and their license plates. This detection phase is crucial as it locates the license plates in the visual field of the camera.
Once a license plate is successfully detected, the next stage involves text recognition. This is achieved through the use of EasyOCR, a library known for its ability to accurately recognize printed text in various languages and formats. EasyOCR processes the image of the detected license plate to extract the alphanumeric characters printed on it.
Finally, the text extracted from the license plate, which typically includes numbers and letters that form the vehicle’s registration information, is then associated with the identified vehicle. This linkage allows the system to catalog each vehicle along with its corresponding registration details, facilitating tasks such as traffic monitoring, automated toll collection, or enhanced security measures at checkpoints. The integration of these technologies—YoloV5 and EasyOCR—enables the system to function efficiently in real-time applications.