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Artificial Mercosur license plates dataset

Silvano, Gilles Velleneuve Trindade, Ivanovitch, Silva orcid logoORCID: 0000-0002-0116-6489, Ribeiro, Vinícius Campos Tinoco orcid logoORCID: 0000-0002-6033-3320, Rodrigues Greati, Vitor orcid logoORCID: 0000-0003-3240-386X, Bezerra, Aguinaldo orcid logoORCID: 0000-0002-0494-8152, Takako Endo, Patricia orcid logoORCID: 0000-0002-9163-5583 and Lynn, Theo orcid logoORCID: 0000-0001-9284-7580 (2020) Artificial Mercosur license plates dataset. Data in Brief, 33 . ISSN 2352-3409

Mercosur (a.k.a. Mercosul) is a trade bloc comprising five South American countries. In 2018, a unified Mercosur license plate model was rolled out. Access to large volumes of ground truth Mercosur license plates with sufficient presentation variety is a significant challenge for training supervised models for license plate detection (LPD) in automatic license plate recognition (ALPR) systems. To address this problem, a Mercosur license plate generator was developed to generate artificial license plate images meeting the new standard with sufficient variety for ALPR training purposes. This includes images with variation due to occlusions and environmental conditions. An embedded system was developed for detecting legacy license plates in images of real scenarios and overwriting these with artificially generated Mercosur license plates. This data set comprises 3,829 images of vehicles with synthetic license plates that meet the new Mercosur standard in real scenarios, and equivalent number of text files containing label information for the images, all organized in a CSV file with compiled image file paths and associated labels.
Item Type:Article (Published)
Additional Information:Article number: 106554
Uncontrolled Keywords:License plates images; Mercosur license plates; Automated license plate recognition; license plate detection; Number Plate Detection; Smart Cities; Deep Learning; Synthetic Data
DCU Faculties and Centres:DCU Faculties and Schools > DCU Business School
Official URL:https://dx.doi.org/10.1016/j.dib.2020.106554
Copyright Information:© 2020 The Authors. Open Access article (CC-BY 4.0)
ID Code:27520
Deposited On:09 Aug 2022 12:06 by Thomas Murtagh . Last Modified 09 Aug 2022 12:06

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