# BLPR: Robust License Plate Recognition under Viewpoint and Illumination Variations via Confidence-Driven VLM Fallback

arXiv: https://arxiv.org/abs/2604.09927 · HTML: https://mlsanity.com/paper/2604.09927/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector](https://mlsanity.com/paper/1802.09567/) (2018)
- [A Robust Attentional Framework for License Plate Recognition in the Wild](https://mlsanity.com/paper/2006.03919/) (2020)
- [Benchmark for License Plate Character Segmentation](https://mlsanity.com/paper/1607.02937/) (2016)
- [On the Cross-dataset Generalization in License Plate Recognition](https://mlsanity.com/paper/2201.00267/) (2022)
- [A First Look at Dataset Bias in License Plate Recognition](https://mlsanity.com/paper/2208.10657/) (2022)
- [Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation](https://mlsanity.com/paper/2601.07671/) (2026)
- [CLOCR-C: Context Leveraging OCR Correction with Pre-trained Language Models](https://mlsanity.com/paper/2408.17428/) (2024)
- [Recognizing License Plates in Real-Time](https://mlsanity.com/paper/1906.04376/) (2019)
- [License Plate Images Generation with Diffusion Models](https://mlsanity.com/paper/2501.03374/) (2025)
- [Comparing OCR Pipelines for Folkloristic Text Digitization](https://mlsanity.com/paper/2507.19092/) (2025)

## Abstract (arXiv)

Robust license plate recognition in unconstrained environments remains a significant challenge, particularly in underrepresented regions with limited data availability and unique visual characteristics, such as Bolivia. Recognition accuracy in real-world conditions is often degraded by illumination changes and viewpoint distortion. To address these challenges, we introduce BLPR, a deep learning-based License Plate Detection and Recognition (LPDR) framework designed for Bolivian license plates. BLPR adaptively applies geometric rectification, illumination correction, and VLM-assisted fallback based on image-condition and confidence cues. The proposed system uses a YOLO-based detector pretrained on synthetic data generated in Blender to simulate extreme perspectives and lighting conditions, and is fine-tuned on street-level data collected in La Paz, Bolivia. Detected plates are processed by a YOLO-based character recognizer, while a lightweight vision-language model (Gemma3 4B) is selectively triggered in ambiguous cases as a confidence-driven fallback mechanism. We also introduce the first publicly available Bolivian LPDR dataset for academic research, supporting evaluation under diverse viewpoint and illumination conditions. The system achieves a character-level recognition accuracy of 89.6% on real-world data, demonstrating its effectiveness for deployment in challenging urban environments.