License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above factors, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. Existing works have not studied this challenging problem. In this work, we first address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high labor cost. To handle this problem, we propose a novel task-level disentanglement generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation into the text generation and background generation in an end-to-end manner to effectively ensure diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks.

翻译：车牌识别在许多实际应用中扮演着关键角色，但大型车辆的车牌因分辨率低、污染、光照不足、遮挡等因素难以识别。为克服上述问题，交通管理部门通常在车辆尾部引入大尺寸车牌。然而，大尺寸车牌在位置、尺寸和样式上具有高度非标准性，导致其多样性显著。此外，背景区域包含大量噪声信息，严重干扰车牌字符的识别。现有研究尚未涉及这一具有挑战性的问题。本文首次针对大尺寸车牌识别问题展开研究，并构建了一个包含9342张图像的公开数据集，覆盖了真实场景中的大部分挑战。然而，构建的数据仍不足以训练深度学习大尺寸车牌识别方法，且大规模训练数据的构建耗时且人力成本高昂。为解决此问题，我们提出了一种基于解耦生成网络（DGNet）的新型任务级解耦生成框架，该框架以端到端方式将生成过程解耦为文本生成和背景生成，有效确保多样性与完整性，从而实现鲁棒的大尺寸车牌识别。在构建的数据集上进行了大量实验，并在三个代表性文本识别框架中验证了所提方法的有效性。