Detection and Recognition for Iraqi Modern License Plate Using Deep Learning Approach - Zakawa Hasan, Mayaf; Saeed Al-Ali, Salim Ganim (Statistics, Optimization & Information Computing, 2025)
In recent years, as the accuracy of deep learning techniques has become spectacular, object identification and recognition has recently become an increasingly popular target of computer vision applications. Among them, automatic number plate recognition (ANPR) has attracted much attention and is already a popular subject of research, but is still difficult in cases where there are few public datasets and differing plate formats. In this paper, the pipeline and a combination of YOLOv11n for detection and LPRNet as optical character recognition is proposed as deep learning-based. The experiments are performed on a dataset collected to perform the identification and detection of new Iraqi license plates. Our method had good detection performance on our dataset, in spite of applying cross-dataset pre-trained detection weights based on a CCPD dataset showing that it had good cross-domain generalization. In the recognition stage, an LPRNet model, the training and evaluation of which was conducted on our collected data only. The accuracy of detector and recognizer of this system was 98.4% and 99.6% respectively. The findings emphasize the power of deep learning models to perform cross domain ALPR tasks, and are an indication that future expansion of datasets will result in an increase in robustness on a variety of real-world circumstances.