# Global License Plate Dataset

arXiv: https://arxiv.org/abs/2405.10949 · HTML: https://mlsanity.com/paper/2405.10949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Scene Text Recognition with Permuted Autoregressive Sequence Models](https://mlsanity.com/paper/2207.06966/) (2022)
- [Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition](https://mlsanity.com/paper/1811.00751/) (2018)
- [What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels](https://mlsanity.com/paper/2103.04400/) (2021)
- [Rethinking and Designing a High-performing Automatic License Plate Recognition Approach](https://mlsanity.com/paper/2011.14936/) (2020)
- [Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition](https://mlsanity.com/paper/2304.04653/) (2023)
- [Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs](https://mlsanity.com/paper/1601.05610/) (2016)
- [Why You Should Try the Real Data for the Scene Text Recognition](https://mlsanity.com/paper/2107.13938/) (2021)
- [Indian Licence Plate Dataset in the wild](https://mlsanity.com/paper/2111.06054/) (2021)
- [Indian Commercial Truck License Plate Detection and Recognition for Weighbridge Automation](https://mlsanity.com/paper/2211.13194/) (2022)

## Abstract (arXiv)

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.