A Deep Learning Framework for Automated Vehicle License Plate Recognition in Nigeria

Nigeria's rapid urbanization has placed immense strain on mobility, intensifying traffic congestion and vehicle-related security challenges. In this context, advanced Intelligent Transportation Systems (ITS) are increasingly essential. Automatic License Plate Recognition (ALPR) offers a powerful means to monitor and manage vehicles, yet its real-world deployment in Nigeria faces distinctive environmental and infrastructural constraints: plate formats vary widely, lighting is uneven, and plate condition deteriorates. This work presents a robust, end-to-end ALPR system designed specifically for Nigerian conditions. We implement a two-stage pipeline that leverages state-of-the-art deep learning components: (1) a fine-tuned, attention-centric YOLOv12 model to localize license plates with high precision, and (2) the EasyOCR engine to transcribe alphanumeric characters. The YOLOv12 detector was fine-tuned on the publicly available Nigerian License Plate Dataset comprising 2,200 license plate images and strengthened with extensive data augmentation to mirror real-world variability, including night scenes, glare, occlusion, and plate wear. We rigorously evaluate the system on a dedicated test split. The model achieves a mean Average Precision (mAP@.50) of 98.0% for plate detection and a Character Recognition Rate (CRR) of 96.0% for transcription, demonstrating not only competitive accuracy by contemporary standards but also the capability to operate in real time on standard hardware. The results indicate that the proposed architecture supports large-scale deployment for traffic monitoring, automated tolling, and law enforcement, offering timely insights and operational efficiency. The principal contribution of this work is a validated, high-performance ALPR framework tailored to the unique challenges faced by a developing African nation, which provides a practical benchmark and reference for future research and deployment in the region.