Global License Plate Dataset
This dataset serves as a benchmark for researchers and practitioners in computer vision working on license plate recognition, traffic monitoring, and related applications, though it is incremental as it builds on existing datasets by expanding scale and diversity.
The authors tackled the problem of advancing road safety and automation by introducing the Global License Plate Dataset (GLPD), which includes over 5 million images from 74 countries with detailed annotations, and they provided baseline models for license plate recognition.
In the pursuit of advancing the state-of-the-art (SOTA) in road safety, traffic monitoring, surveillance, and logistics automation, we introduce the Global License Plate Dataset (GLPD). The dataset consists of over 5 million images, including diverse samples captured from 74 countries with meticulous annotations, including license plate characters, license plate segmentation masks, license plate corner vertices, as well as vehicle make, colour, and model. We also include annotated data on more classes, such as pedestrians, vehicles, roads, etc. We include a statistical analysis of the dataset, and provide baseline efficient and accurate models. The GLPD aims to be the primary benchmark dataset for model development and finetuning for license plate recognition.