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.
Abstract (translated)
自动车牌识别（Automatic License Plate Recognition，ALPR）是一个由于其实用性广泛而备受研究的课题。尽管最近的研究使用合成图像来改进车牌识别（License Plate Recognition，LPR）结果，但仍存在一些限制。这项工作通过全面探索真实数据和合成数据的整合来解决这些限制，以提高LPR性能。我们对16种光学字符识别（OCR）模型进行了基准测试，涉及从不同地区获取的12个公共数据集。我们的研究得出了一些关键发现： 首先，大量使用合成数据显著提升了模型在内部分布和跨分布场景中的表现。 其次，我们探讨了三种不同的生成合成数据的方法：基于模板的生成、字符排列以及利用生成对抗网络（GAN）模型，每种方法都对性能提升有重大贡献。这几种方法结合使用的综合效果明显，使得从端到端的结果超越现有的最先进的方法和已建立的商业系统。 此外，我们的实验强调了合成数据在缓解由于训练数据量有限带来的挑战方面的有效性，在使用少量原始训练数据的情况下也能获得显著结果。 最后，我们研究了不同模型之间准确性和速度之间的权衡，并根据每个内部分布和跨分布场景识别出达到最佳平衡的那些模型。
URL
https://arxiv.org/abs/2601.07671
