This dataset, called UFPR-ALPR dataset, includes 4,500 fully annotated images (over 30,000 LP characters) from 150 vehicles in real-world scenarios where both vehicle and camera (inside another vehicle) are moving. It has been introduced in our IJCNN paper [PDF].

The images were acquired with three different cameras and are available in the Portable Network Graphics (PNG) format with size of 1,920 × 1,080 pixels. The cameras used were: GoPro Hero4 Silver, Huawei P9 Lite and iPhone 7 Plus.

We collected 1,500 images with each camera, divided as follows:

  • 900 of cars with gray LP;
  • 300 of cars with red LP;
  • 300 of motorcycles with gray LP.

The dataset is split as follows: 40% for training, 40% for testing and 20% for validation. Every image has the following annotations available in a text file: the camera in which the image was taken, the vehicle’s position and information such as type (car or motorcycle), manufacturer, model and year; the identification and position of the LP, as well as the position of its characters. The full details are in our paper.

How to obtain the Dataset

The UFPR-ALPR dataset is released for academic research only and is free to researchers from educational or research institutes for non-commercial purposes.

Please click here for more info about obtaining the dataset.


If you use the UFPR-ALPR dataset in your research please cite our paper:

  • R. Laroca, E. Severo, L. A. Zanlorensi, L. S. Oliveira, G. R. Gonçalves, W. R. Schwartz, and D. Menotti, “A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector” in 2018 International Joint Conference on Neural Networks (IJCNN), July 2018, pp. 1–10. [IEEE Xplore] [PDF] [BibTeX] [Supplementary Files] [Presentation] [Video Demonstration]


Please contact Rayson Laroca (rblsantos@inf.ufpr.br) with questions or comments.