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.

翻译：自动车牌识别因其广泛的实际应用而成为常见的研究课题。尽管近期研究使用合成图像来改进车牌识别效果，但这些工作仍存在若干局限性。本研究通过全面探索真实数据与合成数据的融合来提升车牌识别性能，从而解决这些限制。我们对16个光学字符识别模型进行了基准测试，涉及来自不同地区的12个公共数据集。我们的研究得出了若干关键发现：首先，大规模引入合成数据能显著提升模型在数据集内和跨数据集场景下的性能。我们研究了三种不同的合成数据生成方法：基于模板的生成、字符排列以及使用生成对抗网络模型，每种方法都对性能提升有显著贡献。这些方法的组合使用展现出显著的协同效应，实现了超越现有先进方法和成熟商业系统的端到端识别效果。我们的实验还证明了合成数据在缓解训练数据不足挑战方面的有效性，即使仅使用原始训练数据的很小部分也能获得显著成果。最后，我们探究了不同模型在准确率与速度之间的权衡关系，确定了在数据集内和跨数据集场景下均能达到最佳平衡的模型。