Global License Plate Dataset

TL;DR

The paper introduces the Global License Plate Dataset (GLPD), a comprehensive collection of over 5 million images from 74 countries, designed to advance license plate recognition and related applications.

Contribution

It provides a large, diverse, and meticulously annotated dataset along with baseline models, serving as a benchmark for license plate recognition research.

Findings

Dataset contains over 5 million images from 74 countries.

Includes detailed annotations like characters, masks, and vehicle info.

Baseline models demonstrate the dataset's utility for accurate license plate recognition.

Abstract

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.