{"url":"/dataset/ufpr-alpr","name":"UFPR-ALPR","full_name":null,"description_markdown":"This dataset includes 4,500 fully annotated images (over 30,000 license plate characters) from 150 vehicles in real-world scenarios where both the vehicle and the camera (inside another vehicle) are moving.\r\n\r\nThe images were acquired with three different cameras and are available in the Portable Network Graphics (PNG) format with a size of 1,920 × 1,080 pixels. The cameras used were: GoPro Hero4 Silver, Huawei P9 Lite, and iPhone 7 Plus.\r\n\r\nWe collected 1,500 images with each camera, divided as follows:\r\n\r\n\t- 900 of cars with gray license plates;\r\n\t- 300 of cars with red license plates;\r\n\t- 300 of motorcycles with gray license plates.\r\n\r\nThe 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 license plate, as well as the position of its characters. \r\n\r\nSource: [A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector](/paper/a-robust-real-time-automatic-license-plate)","description_withheld":null,"homepage":"https://web.inf.ufpr.br/vri/databases/ufpr-alpr/","introduced_date":"2018-10-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-robust-real-time-automatic-license-plate","title":"A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector","first_author":"Rayson Laroca","url":null},"license":{"name":"Research Only","url":"https://web.inf.ufpr.br/vri/databases/ufpr-alpr/license-agreement/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Object Tracking","url":"/task/object-tracking","datasets_with_task":"/datasets/task/object-tracking"},{"name":"Optical Character Recognition (OCR)","url":"/task/optical-character-recognition","datasets_with_task":"/datasets/task/optical-character-recognition"},{"name":"Scene Text Recognition","url":"/task/scene-text-recognition","datasets_with_task":"/datasets/task/scene-text-recognition"},{"name":"License Plate Recognition","url":"/task/license-plate-recognition","datasets_with_task":"/datasets/task/license-plate-recognition"},{"name":"License Plate Detection","url":"/task/license-plate-detection","datasets_with_task":"/datasets/task/license-plate-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["UFPR-ALPR"],"data_loaders":[{"repo":"https://github.com/ultralytics/yolov5","url":"https://github.com/ultralytics/yolov5","frameworks":["pytorch"]}],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/license-plate-recognition-on-ufpr-alpr","task":"License Plate Recognition","dataset_variant":"UFPR-ALPR","rows":3,"metrics":["Rank-1 Recognition Rate"],"first_row_in_archive_order":{"model":"Character Time-series Matching For Robust License Plate Recognition","paper":"/paper/character-time-series-matching-for-robust-1","metrics":{"Rank-1 Recognition Rate":"96.7"},"code_links":[{"title":"chequanghuy/Character-Time-series-Matching","url":"https://github.com/chequanghuy/Character-Time-series-Matching"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/character-time-series-matching-for-robust-1","title":"Character Time-series Matching For Robust License Plate Recognition","date":"2023-07-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/an-efficient-and-layout-independent-automatic","title":"An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector","date":"2019-09-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-robust-real-time-automatic-license-plate","title":"A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector","date":"2018-02-26","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}