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.

You can now check who is downloading our dataset (see here).

References

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

You may also be interested in the extended version of this paper, where we considerably improved our system:

R. Laroca, L. A. Zanlorensi, G. R. Gonçalves, E. Todt, W. R. Schwartz, D. Menotti, “An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO Detector,” arXiv preprint arXiv:1909.01754, 2019. [Webpage] [arXiv] [PDF] [BibTeX]

Additional Results

To enable comparisons with approaches designed specifically for cars (i.e., approaches that do not work for motorcycles), here we separately report the recognition rates obtained on images of cars and motorcycles (see table below). All authors who downloaded the dataset were notified of this update on August 2, 2019.

ALPR System Cars Motorcycles Cars + Motorcycles Sighthound (2018) 58.4% 3.3% 47.4% OpenALPR (2018) 58.0% 22.8% 50.9% Proposed (2018) 72.2% 35.6% 64.9% Proposed-Extended (2019) 95.9% 66.3% 90.0%

ALPR System (with redundancy) Cars Motorcycles Cars + Motorcycles Sighthound (2018) 70.8% 0.0% 56.7% OpenALPR (2018) 89.6% 0.0% 71.7% Proposed (2018) 83.3% 58.3% 78.3% Proposed-Extended (2019) 98.3% 70.0% 92.7%

Contact

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