# Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation

arXiv: https://arxiv.org/abs/2601.07671 · HTML: https://mlsanity.com/paper/2601.07671/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 88 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 23 of 23)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis](https://mlsanity.com/paper/1904.01906/) (2019)
- [Robust Scene Text Recognition with Automatic Rectification](https://mlsanity.com/paper/1603.03915/) (2016)
- [Vision Transformer for Fast and Efficient Scene Text Recognition](https://mlsanity.com/paper/2105.08582/) (2021)
- [Recursive Recurrent Nets with Attention Modeling for OCR in the Wild](https://mlsanity.com/paper/1603.03101/) (2016)
- [Rosetta: Large scale system for text detection and recognition in images](https://mlsanity.com/paper/1910.05085/) (2019)
- [What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels](https://mlsanity.com/paper/2103.04400/) (2021)
- [A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector](https://mlsanity.com/paper/1802.09567/) (2018)
- [MANGO: A Mask Attention Guided One-Stage Scene Text Spotter](https://mlsanity.com/paper/2012.04350/) (2020)
- [An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector](https://mlsanity.com/paper/1909.01754/) (2019)
- [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)
- [Rethinking and Designing a High-performing Automatic License Plate Recognition Approach](https://mlsanity.com/paper/2011.14936/) (2020)
- [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)
- [Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach](https://mlsanity.com/paper/2009.10181/) (2020)
- [Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers](https://mlsanity.com/paper/2305.17313/) (2023)
- [Convolutional Neural Networks for Automatic Meter Reading](https://mlsanity.com/paper/1902.09600/) (2019)
- [Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition](https://mlsanity.com/paper/2302.01427/) (2023)

## Built on by (most cited first, 7 of 7)

- [Toward Unified Fine-Grained Vehicle Classification and Automatic License Plate Recognition](https://mlsanity.com/paper/2604.05271/) (2026)
- [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)
- [Revisiting Vehicle Color Recognition in Long-Tailed Surveillance Scenarios](https://mlsanity.com/paper/2606.13625/) (2026)
- [BLPR: Robust License Plate Recognition under Viewpoint and Illumination Variations via Confidence-Driven VLM Fallback](https://mlsanity.com/paper/2604.09927/) (2026)
- [Evaluating 2D and 3D-Aware Vision Foundation Models for Vehicle Attribute Recognition](https://mlsanity.com/paper/2608.29929/) (2026)
- [Confidence-Aware Ensemble and Long-Word Refinement for Artistic Text Recognition](https://mlsanity.com/paper/2608.29970/) (2026)

## Abstract (arXiv)

Automatic License Plate Recognition is a frequent research topic due to its wide-ranging practical applications. While recent studies use synthetic images to improve License Plate Recognition (LPR) results, there remain several limitations in these efforts. This work addresses these constraints by comprehensively exploring the integration of real and synthetic data to enhance LPR performance. We subject 16 Optical Character Recognition (OCR) models to a benchmarking process involving 12 public datasets acquired from various regions. Several key findings emerge from our investigation. Primarily, the massive incorporation of synthetic data substantially boosts model performance in both intra- and cross-dataset scenarios. We examine three distinct methodologies for generating synthetic data: template-based generation, character permutation, and utilizing a Generative Adversarial Network (GAN) model, each contributing significantly to performance enhancement. The combined use of these methodologies demonstrates a notable synergistic effect, leading to end-to-end results that surpass those reached by state-of-the-art methods and established commercial systems. Our experiments also underscore the efficacy of synthetic data in mitigating challenges posed by limited training data, enabling remarkable results to be achieved even with small fractions of the original training data. Finally, we investigate the trade-off between accuracy and speed among different models, identifying those that strike the optimal balance in each intra-dataset and cross-dataset settings.