# A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

arXiv: https://arxiv.org/abs/1802.09567 · HTML: https://mlsanity.com/paper/1802.09567/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 30 of 56)

- [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)
- [Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition](https://mlsanity.com/paper/2304.04653/) (2023)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Unified Chinese License Plate Detection and Recognition with High Efficiency](https://mlsanity.com/paper/2205.03582/) (2022)
- [Leveraging Model Fusion for Improved License Plate Recognition](https://mlsanity.com/paper/2309.04331/) (2023)
- [Forensic License Plate Recognition with Compression-Informed Transformers](https://mlsanity.com/paper/2207.14686/) (2022)
- [Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection](https://mlsanity.com/paper/2412.13490/) (2024)
- [Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation](https://mlsanity.com/paper/2601.07671/) (2026)
- [Attacking Object Detector Using A Universal Targeted Label-Switch Patch](https://mlsanity.com/paper/2211.08859/) (2022)
- [Toward Enhancing Vehicle Color Recognition in Adverse Conditions: A Dataset and Benchmark](https://mlsanity.com/paper/2408.11589/) (2024)
- [Toward Unified Fine-Grained Vehicle Classification and Automatic License Plate Recognition](https://mlsanity.com/paper/2604.05271/) (2026)
- [Real-time Bangla License Plate Recognition System for Low Resource Video-based Applications](https://mlsanity.com/paper/2108.08339/) (2021)
- [LPLCv2: An Expanded Dataset for Fine-Grained License Plate Legibility Classification](https://mlsanity.com/paper/2604.08741/) (2026)
- [ICPR 2026 Competition on Low-Resolution License Plate Recognition](https://mlsanity.com/paper/2604.22506/) (2026)
- [Histogram of Cell Types: Deep Learning for Automated Bone Marrow Cytology](https://mlsanity.com/paper/2107.02293/) (2021)
- [IR-LPR: Large Scale of Iranian License Plate Recognition Dataset](https://mlsanity.com/paper/2209.04680/) (2022)
- [SamLP: A Customized Segment Anything Model for License Plate Detection](https://mlsanity.com/paper/2401.06374/) (2024)
- [A Decade of You Only Look Once (YOLO) for Object Detection: A Review](https://mlsanity.com/paper/2504.18586/) (2025)
- [ScanBank: A Benchmark Dataset for Figure Extraction from Scanned Electronic Theses and Dissertations](https://mlsanity.com/paper/2106.15320/) (2021)
- [Indian Licence Plate Dataset in the wild](https://mlsanity.com/paper/2111.06054/) (2021)
- [BLPnet: A new DNN model and Bengali OCR engine for Automatic License Plate Recognition](https://mlsanity.com/paper/2202.12250/) (2022)
- [Character Time-series Matching For Robust License Plate Recognition](https://mlsanity.com/paper/2307.11336/) (2023)
- [STEP -- Towards Structured Scene-Text Spotting](https://mlsanity.com/paper/2309.02356/) (2023)
- [A Training-Free Framework for Video License Plate Tracking and Recognition with Only One-Shot](https://mlsanity.com/paper/2408.05729/) (2024)
- [License Plate Images Generation with Diffusion Models](https://mlsanity.com/paper/2501.03374/) (2025)
- [Real-Time Privacy Preservation for Robot Visual Perception](https://mlsanity.com/paper/2505.05519/) (2025)
- [Video-based Vehicle Surveillance in the Wild: License Plate, Make, and Model Recognition with Self Reflective Vision-Language Models](https://mlsanity.com/paper/2508.01387/) (2025)
- [MF-LPR2: Multi-Frame License Plate Image Restoration and Recognition using Optical Flow](https://mlsanity.com/paper/2508.14797/) (2025)
- [Typography-Based Monocular Distance Estimation Framework for Vehicle Safety Systems](https://mlsanity.com/paper/2603.22781/) (2026)

## Abstract (arXiv)

Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications. However, many of the current solutions are still not robust in real-world situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. The Convolutional Neural Networks (CNNs) are trained and fine-tuned for each ALPR stage so that they are robust under different conditions (e.g., variations in camera, lighting, and background). Specially for character segmentation and recognition, we design a two-stage approach employing simple data augmentation tricks such as inverted License Plates (LPs) and flipped characters. The resulting ALPR approach achieved impressive results in two datasets. First, in the SSIG dataset, composed of 2,000 frames from 101 vehicle videos, our system achieved a recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%, respectively) and considerably outperforming previous results (81.80%). Second, targeting a more realistic scenario, we introduce a larger public dataset, called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos and 4,500 frames captured when both camera and vehicles are moving and also contains different types of vehicles (cars, motorcycles, buses and trucks). In our proposed dataset, the trial versions of commercial systems achieved recognition rates below 70%. On the other hand, our system performed better, with recognition rate of 78.33% and 35 FPS.