RECOGNITION SYSTEM

Vehicle identification and verification play a critical role in ensuring the security of controlled environments such as university campuses. At the University of Benin’s Ugbowo Campus, the existing manual vehicle verification process suffers from several limitations, including delays, human error, poor record keeping, and the absence of real-time validation. This study addresses these challenges by designing and implementing an Automated Car Plate Number Recognition (ANPR) system capable of detecting, recognizing, and verifying vehicle license plates using computer vision and optical character recognition (OCR) techniques. The system was developed using the Flask web framework, OpenCV for image preprocessing and license plate localization, and the Tesseract OCR engine for extracting alphanumeric characters. A MySQL database was integrated for storing records of registered vehicles and maintaining logs of recognition events. The system allows security personnel to upload vehicle images through a web interface, after which the application automatically processes the image, recognizes the plate number, checks its validity against the stored records, and provides immediate feedback. Testing of the system using images captured under different conditions such as clear daytime lighting, low light, skewed angles, and motion blur revealed that the system performs accurately under favourable conditions and provides partial or usable information even under challenging scenarios. Although recognition performance decreases with poor image quality, the system still offers considerable improvements over manual verification by reducing reliance on human judgment and enhancing the accuracy, speed, and reliability of vehicle monitoring.