Image-Quality-Aware Enhancement and Syntax-Constrained Error Correction for License Plate Recognition in Complex Traffic Scenes

License plate recognition (LPR) in complex traffic scenes remains sensitive to illumination variation, adverse weather, motion blur, geometric distortion, and low local contrast. This study presents an image-quality-aware framework that integrates YOLOv11n plate localization, selective CLAHE enhancement, PP-OCRv5 recognition, and syntax-constrained post-processing. Each cropped plate is assessed using the mean and standard deviation of the OpenCV (version 4.10.0) CIELAB L channel, and enhancement is applied only when lightness or contrast is insufficient. OCR outputs are subsequently validated using sequence-length checking, position-specific constraints, confusable-character mappings, and a confidence-guided letter-count heuristic for the serial number. Experiments on a study-specific sample of 10,245 CCPD2019 images show that YOLOv11n achieves a test mAP@0.5:0.95 of 0.843 with a 5.5 MB weight file and a recorded workstation detector throughput of 163 FPS. On 1950 plate-containing test images, the complete framework achieves a source-image-level end-to-end exact-match accuracy of 0.6005, compared with 0.5390 for the baseline, corresponding to an improvement of 6.15 percentage points (95% CI: 4.97–7.44). On 1943 matched true-positive detections, character accuracy reaches 0.8474, and the character error rate decreases to 0.1526. Ablation analysis identifies syntax-constrained correction as the principal contributor, whereas selective CLAHE alone yields only a small, statistically non-significant gain. End-to-end throughput remains essentially unchanged at 19.32 FPS, compared with 19.34 FPS for the baseline.