{"url":"/dataset/ssig-segplate","name":"SSIG-SegPlate","full_name":null,"description_markdown":"This dataset aims at evaluating the License Plate Character Segmentation (LPCS) problem. The experimental results of the paper Benchmark for License Plate Character Segmentation were obtained using a dataset providing 101 on-track vehicles captured during the day. The video was recorded using a static camera in early 2015.\r\n\r\nThe 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).\r\n\r\nSource: [Benchmark for License Plate Character Segmentation](/paper/benchmark-for-license-plate-character)","description_withheld":null,"homepage":"http://smartsenselab.dcc.ufmg.br/en/dataset/sense-segplate/","introduced_date":"2016-07-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmark-for-license-plate-character","title":"Benchmark for License Plate Character Segmentation","first_author":"Gabriel Resende Gonçalves","url":null},"license":{"name":"Research Only","url":"http://www.ssig.dcc.ufmg.br/wp-content/uploads/2017/03/SSIG-SegPlate_Database_License_Agreement.pdf"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"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":["SSIG-SegPlate"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/license-plate-recognition-on-ssig-segplate","task":"License Plate Recognition","dataset_variant":"SSIG-SegPlate","rows":2,"metrics":["Rank-1 Recognition Rate"],"first_row_in_archive_order":{"model":"YOLOv2 + Fast-YOLOv2 + CR-NET","paper":"/paper/an-efficient-and-layout-independent-automatic","metrics":{"Rank-1 Recognition Rate":"98.2"},"code_links":[{"title":"brightyoun/TITS-LPST","url":"https://github.com/brightyoun/TITS-LPST"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"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."}