Sense License Plate Character Segmentation Database
This dataset, called Sense SegPlate Database, aims at evaluating the License Plate Character Segmentation (LPCS) problem. The experimental results of the paper Benchmark for License Plate Character Segmentation (link to the research page) were obtained using a dataset providing 101 on-track vehicles captured during the day. The video was recorded using a static camera in the early 2015.
The images of the dataset were acquired with a digital camera in Full-HD and are available in the Portable Network Graphics (PNG) format with 1920×1080 pixels each. The average size of each file is 4.08 Megabytes (a total of 8.60 Gigabytes for the entire dataset). In addition, since there are some approaches that track the car to utilize redundant information to improve the recognition results, we decided to make a dataset with multiples frames per car. In this dataset, there are, on average, 19.80 image frames per vehicle (with a standard deviation of 4.14).
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 licenses signed by students or faculty members.
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.