Hierarchical YOLO with Real-Time Text Recognition for UAE Traffic Signs
Muhammad A Usmani, Youssef Elmadany, Samir Mahmood, Abdullah Azeem, Imran Ahmed Zualkernan · 2025
This paper presents an advanced hierarchical detection and real-time text recognition system tailored for UAE traffic signs. Our system is designed to support critical applications in autonomous vehicle navigation, assistance for visually impaired individuals, and automated traffic sign maintenance. Emphasizing sensor-based applications our approach uniquely incorporates text recognition within the traffic sign analysis pipeline, addressing the complex challenge of bilingual (Arabic-English) signs-a capability not previously explored in the literature.The system employs a three-stage pipeline, combining object detection, symbol detection, and advanced text recognition, all optimized for high-speed, realworld conditions. Trained on DoTaS, a custom dataset with 1,500 UAE traffic sign images, the system achieves a mean Average Precision (mAP) of 0.9124 using YOLOv8. For English text recognition, PARSeq achieves 89.3% word accuracy, setting a new benchmark for real-time, high-accuracy sign interpretation. Operating at around 250 ms per inference (around 4 FPS), our framework enhances safety, accessibility, and efficiency in urban environments.