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

• URL: http://arxiv.org/abs/2601.07671v1
• Date: Mon, 12 Jan 2026 15:52:52 GMT
• Title: Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation
• Authors: Rayson Laroca, Valter Estevam, Gladston J. P. Moreira, Rodrigo Minetto, David Menotti,
• Abstract summary: We explore the integration of real and synthetic data to enhance LPR performance.<n>Massive incorporation of synthetic data substantially boosts model performance in both intra- and cross-dataset scenarios.<n>Experiments underscore the efficacy of synthetic data in mitigating challenges posed by limited training data.
• Score: 3.3637719592955526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Data Value in the Age of Scaling: Understanding LLM Scaling Dynamics Under Real-Synthetic Data Mixtures [32.89034139737846]
  Large language models (LLMs) are built on datasets that blend real and synthetic data.<n> synthetic data offers scalability and cost-efficiency, but it often introduces systematic distributional discrepancies.<n>We propose an effective yet efficient data valuation method that scales to large-scale datasets.
  arXiv  Detail & Related papers  (2025-11-17T17:53:12Z)
• Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data [6.318463500874778]
  We propose a prototype-guided diffusion model to generate high-fidelity synthetic pathology data at scale.<n>Our approach ensures biologically and diagnostically meaningful variations in the generated data.<n>We demonstrate that self-supervised features trained on our synthetic dataset achieve competitive performance despite using 60x-760x less data than models trained on large real-world datasets.
  arXiv  Detail & Related papers  (2025-04-15T21:17:39Z)
• Scaling Laws of Synthetic Data for Language Models [125.41600201811417]
  We introduce SynthLLM, a scalable framework that transforms pre-training corpora into diverse, high-quality synthetic datasets.<n>Our approach achieves this by automatically extracting and recombining high-level concepts across multiple documents using a graph algorithm.
  arXiv  Detail & Related papers  (2025-03-25T11:07:12Z)
• License Plate Images Generation with Diffusion Models [4.151073288078749]
  We propose to synthesize realistic license plates (LPs) using diffusion models inspired by video generation.<n>We have prepared a synthetic dataset consisting of 10,000 LP images, publicly available at https://zenodo.org/doi/10.528/zenodo.13342102.
  arXiv  Detail & Related papers  (2025-01-06T20:22:18Z)
• SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data [78.70620682374624]
  We introduce SynFER, a novel framework for synthesizing facial expression image data based on high-level textual descriptions.<n>To ensure the quality and reliability of the synthetic data, we propose a semantic guidance technique and a pseudo-label generator.<n>Results validate the efficacy of our approach and the synthetic data.
  arXiv  Detail & Related papers  (2024-10-13T14:58:21Z)
• Hybrid Training Approaches for LLMs: Leveraging Real and Synthetic Data to Enhance Model Performance in Domain-Specific Applications [0.0]
  This research explores a hybrid approach to fine-tuning large language models (LLMs) By leveraging a dataset combining transcribed real interactions with high-quality synthetic sessions, we aimed to overcome the limitations of domain-specific real data. The study evaluated three models: a base foundational model, a model fine-tuned with real data, and a hybrid fine-tuned model.
  arXiv  Detail & Related papers  (2024-10-11T18:16:03Z)
• Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models [89.88010750772413]
  Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs) Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
  arXiv  Detail & Related papers  (2024-06-18T08:38:59Z)
• Learning from Synthetic Data for Visual Grounding [55.21937116752679]
  We show that SynGround can improve the localization capabilities of off-the-shelf vision-and-language models.<n>Data generated with SynGround improves the pointing game accuracy of a pretrained ALBEF and BLIP models by 4.81% and 17.11% absolute percentage points, respectively.
  arXiv  Detail & Related papers  (2024-03-20T17:59:43Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques [7.967995669387532]
  generative models have emerged as a promising solution for generating synthetic datasets that can replace or augment real-world data. We propose three novel post-processing techniques to improve the quality and diversity of the synthetic dataset. Experiments show that Gap Filler (GaFi) effectively reduces the gap with real-accuracy scores to an error of 2.03%, 1.78%, and 3.99% on the Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets, respectively.
  arXiv  Detail & Related papers  (2023-05-17T10:50:38Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.