GroupPlate: Toward Multi-Category License Plate Recognition

License Plate Detection and Recognition (LPDR) is widely used in Intelligent Transportation Systems (ITS). Although there are typically multiple categories of license plates, the majority of existing research cannot be applied to multi-category plates due to that existing methods are not optimised for multi-category plate scenarios and the scarcity of large-scale multi-category plate datasets. In this paper, we propose a multi-category license plate recognition framework called GroupPlate, which consists of Group Module and Indirect Supervision Module, making full use of the implicit and explicit grouping information of license plate. In addition, the Category Decouple Module is intended to decouple the grouping information from the original features, allowing the decoder to concentrate on character features. Simultaneously, we propose a large-scale All-Category license Plate detection and recognition Dataset (ACPD) for vehicles on the Chinese mainland, which also includes annotations of plates' categories. Considering the domain gap between synthetic data and real data, we propose a simple but effective strategy called Feature Shift to mitigate the performance degradation caused by this gap. Experiments demonstrate that GroupPlate achieves the comparable performance to the existing methods on single-category license plate dataset and outperforms our baseline on the multi-category license plates dataset. Ablation experiments demonstrate the effectiveness of the modules in GroupPlate. Extensive results demonstrate that the dataset we proposed can mitigate the problem of models trained on a single-category license plate dataset failing to recognize multi-category license plates, and that our model can generalizes well to unseen categories. The work will be available at https://github.com/YilinGao-SHU/ACPD.