{"url":"/dataset/aolp","name":"AOLP","full_name":"Application-oriented License Plate","description_markdown":"The application-oriented license plate (**AOLP**) benchmark database has 2049 images of Taiwan license plates. This database is categorized into three subsets: access control (AC) with 681 samples, traffic law enforcement (LE) with 757 samples, and road patrol (RP) with 611 samples. AC refers to the cases that a vehicle passes a fixed passage with a lower speed or full stop. This is the easiest situation. The images are captured under different illuminations and different weather conditions. LE refers to the cases that a vehicle violates traffic laws and is captured by roadside camera. The background are really cluttered, with road sign and multiple plates in one image. RP refers to the cases that the camera is held on a patrolling vehicle, and the images are taken with arbitrary viewpoints and distances.\r\n\r\nSource: [Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs](https://arxiv.org/abs/1601.05610)\r\nImage Source: [http://aolpr.ntust.edu.tw/lab/index.html](http://aolpr.ntust.edu.tw/lab/index.html)","description_withheld":null,"homepage":"http://aolpr.ntust.edu.tw/lab/index.html","introduced_date":"2022-06-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/0-1-deep-neural-networks-via-block-coordinate","title":"0/1 Deep Neural Networks via Block Coordinate Descent","first_author":"HUI ZHANG","url":null},"license":{"name":"BEK 0711","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"License Plate Recognition","url":"/task/license-plate-recognition","datasets_with_task":"/datasets/task/license-plate-recognition"}],"languages":[],"variants":["AOLP-RP","AOLP"],"data_loaders":[{"repo":"https://github.com/ThecoderPinar/Autonomous-Plate-Recognition","url":"https://github.com/ThecoderPinar/Autonomous-Plate-Recognition","frameworks":["tf","pytorch"]}],"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-aolp-rp","task":"License Plate Recognition","dataset_variant":"AOLP-RP","rows":3,"metrics":["Average Recall"],"first_row_in_archive_order":{"model":"SNIDER","paper":"/paper/snider-single-noisy-image-denoising-and","metrics":{"Average Recall":"99.18"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/license-plate-recognition-on-aolp","task":"License Plate Recognition","dataset_variant":"AOLP","rows":1,"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":"99.2"},"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/practical-license-plate-recognition-in","title":"Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution","date":"2019-10-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/snider-single-noisy-image-denoising-and","title":"SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition","date":"2019-10-09","rows_on_this_dataset":1,"code_links":0,"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/license-plate-detection-and-recognition-in","title":"License Plate Detection and Recognition in Unconstrained Scenarios","date":"2018-09-01","rows_on_this_dataset":1,"code_links":1,"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."}