Automatic License Plate Recognition (ALPR) in unconstrained traffic environments requires simultaneously addressing two fundamental challenges: reliable localization of small and degraded license plates and accurate decoding of visually ambiguous character sequences. This paper presents a hierarchical multi-stage framework that combines deep-learning-based detection, geometric normalization, dual-channel recognition, and structured post-correction to improve recognition robustness under diverse real-world conditions. A systematic ablation study involving five configurations (A0–A4) demonstrates the effectiveness of the proposed architecture across three benchmark datasets. On the UC3M-LP dataset, exact-match accuracy increases from 45.2% to 88.3%, while achieving 91.6% partial accuracy and a zero detection-miss rate. The framework further attains 95% exact-match accuracy on controlled European license plate crops and 93% on a large-scale custom dataset. In addition, we identify systematic evaluation artifacts in partially annotated benchmarks, showing that truncated ground-truth labels can underestimate genuine character-level improvements. The proposed framework supports multiple license plate formats through a configurable structural template library, and preliminary experiments on a small Arabic-script subset suggest potential extensibility without full model retraining. To ensure full reproducibility, all source code and evaluation resources are publicly released.
Issaoui et al. (Mon,) studied this question.