TITLE:
HPR-Net: A High-Precision Pipeline with Oriented Detection and Transformer-Based Recognition for Robust License Plate Recognition
AUTHORS:
Bertrand Tahte, Alain Djimeli, Geh Wilson Ejuh
KEYWORDS:
Deep Learning, Computer Vision, Oriented Object Detection, YOLOv8, Transformer
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.18 No.4,
October
13,
2026
ABSTRACT: Automatic Number Plate Recognition is a fundamental technology for intelligent transportation systems, urban management, and public security, although its performance degrades significantly in complex real-world conditions, often characterized as unconstrained scenarios. This research paper tackles two fundamental limitations of current state-of-the-art systems by addressing, on the one hand, the geometric inaccuracy of standard horizontal bounding box detectors, which systematically fail when faced with oblique viewing angles by capturing excessive background noise, and, on the other hand, the structural rigidity of common sequential recognition systems, which prove inadequate for the diverse multiline license plate formats found globally. To overcome these complex challenges, the HPR-Net (High-Precision Recognition Network) system is proposed as a modernized two-stage pipeline that synergistically combines cutting-edge deep learning components. The first stage introduces a high-precision-oriented object detector, based on the YOLOv8-OBB architecture, specifically modified to regress the rotation angle of the plate, thereby guaranteeing ultra-precise geometric localization via a loss function sensitive to spatial asymmetry. The second stage utilizes a Transformer-based recognition system that operates on the rectified image of the plate by exploiting a two-dimensional cross-attention mechanism to dynamically and flexibly handle various structural layouts. Exhaustive experimental evaluations conducted on an aggregate of complex public datasets demonstrate the superiority of this approach, with the HPR-Net system achieving an average end-to-end recognition accuracy of 96.2% (±0.20%, macro-average across 3 independent training runs), outperforming baseline benchmarks by 7.3 percentage points. Ablation studies rigorously validate that both the oriented detector and the Transformer-based recognition system are imperative to achieve this new level of performance, thus offering a truly universal solution for smart infrastructures.