Abstract
Despite significant advancements in License Plate Recognition (LPR) through deep learning, most improvements rely on high-resolution images with clear characters. This scenario does not reflect real-world conditions where traffic surveillance often captures low-resolution and blurry images. Under these conditions, characters tend to blend with the background or neighboring characters, making accurate LPR challenging. To address this issue, we introduce a novel loss function, Layout and Character Oriented Focal Loss (LCOFL), which considers factors such as resolution, texture, and structural details, as well as the performance of the LPR task itself. We enhance character feature learning using deformable convolutions and shared weights in an attention module and employ a GAN-based training approach with an Optical Character Recognition (OCR) model as the discriminator to guide the super-resolution process. Our experimental results show significant improvements in character reconstruction quality, outperforming two state-of-the-art methods in both quantitative and qualitative measures. Our code is publicly available at this https URL
Abstract (translated)
尽管通过深度学习在车牌识别（LPR）方面取得了显著的进步，但大多数改进都依赖于高分辨率、清晰字符的图像。这种情况并不代表实际情况，因为交通监控通常捕捉低分辨率、模糊的图像。在这种情况下，字符往往与背景或相邻字符融合在一起，使得准确的车牌识别（LPR）变得具有挑战性。为了解决这个问题，我们引入了一个新的损失函数——布局和字符定向焦点损失（LCOFL），它考虑了分辨率、纹理和结构细节等因素，以及LPR任务的性能本身。我们通过变形卷积和注意力模块中的共享权重来提高字符特征学习，并使用基于GAN的训练方法，其中OCR模型作为判别器来引导超分辨率过程。我们的实验结果表明，在定量和定性指标上，车牌重建质量都有显著的提高，超越了两个最先进的解决方案。我们的代码公开可用，在这个https URL。
URL
https://arxiv.org/abs/2408.15103
