This dataset, called Sense-ALPR Database, was created to help researchers evaluating automatic license plate recognition problems. The data for the paper Real-time Automatic License Plate Recognition Through Deep Multi-Task Networks (link to the research page) was captured during the day using two cameras: one placed static while recording the vehicles that were passing by and another placed within a vehicle that moved accross the city.
The images of the dataset are in Full-HD and are available in the Portable Network Graphics (PNG) format with 1920×1080 pixels each. The entire dataset contains a total of 35 Gigabytes. There are 3,595 training samples, 2,360 testing samples and 705 validation samples, totalizing 6,660 images with 8,683 license plates from 815 different vehicles. The license plates have sizes varying from 5×12 pixels to 86×196 pixels. On average, the license plates images have size of 22×57 pixels (aspect ratio of 0.38). The average size of each file is 2.4 MB.
To be able to download the dataset, please read carefully this agreement, fill it and send it back to one of the suggested e-mails. The license agreement MUST be reviewed and signed by the individual or entity authorized to make legal commitments on behalf of the institution or corporation (e.g., Department or Administrative Head or similar). We cannot accept agreements signed by students or faculty members.
Note: in the SSIG-ALPR dataset, only images in the folder named “Samples_to_Show” can be used for illustrations of scholarly posters, presentations or shown in publications.
You should cite the following paper if you use this dataset in your work.
In: Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2018.
License Plate Recognition based on Temporal Redundancy Inproceedings
In: IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 1-5, 2016.
Benchmark for License Plate Character Segmentation Journal Article
In: Journal of Electronic Imaging, 25 (5), pp. 1-5, 2016, ISBN: 1017-9909.
License Plate Recognition based on Temporal Redundancy Masters Thesis
Federal University of Minas Gerais, 2016.
In: Workshop em Visao Computacional (WVC), pp. 1-5, 2015.
In: Iberoamerican Congress on Pattern Recognition (CIARP), pp. 454-461, Springer International Publishing, 2014.
In: International Journal of Computer Science and Information Technology, 5 , pp. 39-52, 2013.