End-to-End License Plate Detection and Recognition in Iraq Using a Detection Transformer and OCR

Automatic license plate recognition (ALPR) is essential for intelligent transportation systems, traffic monitoring, and law enforcement. Although deep learning has significantly progressed ALPR, the adaptation of these techniques to the specific characteristics of Iraqi plates presents challenges due to the variety of fonts, differing plate dimensions, and intricate real-world driving conditions. This paper presents an end-to-end Automatic License Plate Recognition (ALPR) framework specifically designed for Iraqi license plates. This system integrates a Detection Transformer (DETR) for the identification of plates alongside a Convolutional Recurrent Neural Network (CRNN)-based Optical Character Recognition (OCR) module for the purpose of character recognition. The system is trained and evaluated on a newly developed dataset of 1,000 annotated images representing diverse driving scenarios in Iraq. It achieves a mean Average Precision (mAP@[.5:.95]) of 0.91 for detection and a full-plate OCR accuracy of 93%. The accuracy of DETR surpasses that of YOLOv5, Faster R-CNN, and SSD, as demonstrated by comparative experiments. Conversely, a lightweight transformer (DeiT-Tiny-Det) approaches DETR's performance at faster inference velocities, illustrating a practical trade-off between speed and precision. Ablation studies confirm the importance of robust detection for end-to-end accuracy, while error analysis shows that low-light and character-level confusions remain the main challenges. The results indicate that transformer-based detectors, in conjunction with specialised OCR models, yield dependable region-specific ALPR appropriate for real-world application.