Intelligent License Plate Recognition System for Smart Campus Access Control

Abstract

Modern academic campuses face a growing tension between convenient vehicle access and increasing demands for security and operational efficiency. At scale, manual gate checks are slow, error-prone, and difficult to audit. This paper presents the design, implementation, and evaluation of an intelligent License Plate Recognition (LPR) system for smart-campus access control. The system integrates a fine-tuned YOLOv8 detector for vehicle and plate localization, the EasyOCR engine for real-time plate-text recognition, a MySQL relational database that matches recognized plates against authorized and blacklisted vehicle lists, and a Django web dashboard that provides administrative visibility. A blacklist-aware alerting subsystem dispatches SMS and e-mail notifications whenever an unauthorized or blacklisted vehicle is detected. The novelty of this work lies not in any single algorithmic component but in the complete, reproducible, open-source integration of detection, recognition, database matching, logging, and alerting into one deployable framework adapted to local plate formats and campus operational requirements. The detector was trained on a combination of public benchmarks and a locally collected campus dataset and evaluated on a held-out test set of 850 images, achieving a plate-detection mAP@0.5 of 0.942, precision of 0.945, recall of 0.938, full-plate OCR accuracy of 92.1%, end-to-end recognition accuracy of 94.8%, and an average throughput of 23.5 FPS on commodity hardware. The architecture is modular, scalable, and deployable on Commercial Off-The-Shelf (COTS) hardware, offering an affordable and replicable solution for budget-constrained institutions.