{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-robust-real-time-automatic-license-plate","title":"A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector","arxiv_id":"1802.09567","date":"2018-02-26","proceeding":null,"authors":["Rayson Laroca","Evair Severo","Luiz A. Zanlorensi","Luiz S. Oliveira","Gabriel Resende Gonçalves","William Robson Schwartz","David Menotti"],"abstract":"Automatic License Plate Recognition (ALPR) has been a frequent topic of\nresearch due to many practical applications. However, many of the current\nsolutions are still not robust in real-world situations, commonly depending on\nmany constraints. This paper presents a robust and efficient ALPR system based\non the state-of-the-art YOLO object detector. The Convolutional Neural Networks\n(CNNs) are trained and fine-tuned for each ALPR stage so that they are robust\nunder different conditions (e.g., variations in camera, lighting, and\nbackground). Specially for character segmentation and recognition, we design a\ntwo-stage approach employing simple data augmentation tricks such as inverted\nLicense Plates (LPs) and flipped characters. The resulting ALPR approach\nachieved impressive results in two datasets. First, in the SSIG dataset,\ncomposed of 2,000 frames from 101 vehicle videos, our system achieved a\nrecognition rate of 93.53% and 47 Frames Per Second (FPS), performing better\nthan both Sighthound and OpenALPR commercial systems (89.80% and 93.03%,\nrespectively) and considerably outperforming previous results (81.80%). Second,\ntargeting a more realistic scenario, we introduce a larger public dataset,\ncalled UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos\nand 4,500 frames captured when both camera and vehicles are moving and also\ncontains different types of vehicles (cars, motorcycles, buses and trucks). In\nour proposed dataset, the trial versions of commercial systems achieved\nrecognition rates below 70%. On the other hand, our system performed better,\nwith recognition rate of 78.33% and 35 FPS.","url_abs":"http://arxiv.org/abs/1802.09567v6","url_pdf":"http://arxiv.org/pdf/1802.09567v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-robust-real-time-automatic-license-plate","repo_url":"https://github.com/ikigai-aa/Automatic-License-Plate-Recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-robust-real-time-automatic-license-plate","repo_url":"https://github.com/FaceOnLive/License-Plate-Recognition-SDK-Android","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"license-plate-detection","task_name":"License Plate Detection"},{"task_slug":"license-plate-recognition","task_name":"License Plate Recognition"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"}],"methods":[{"method_slug":"cr-net","method_name":"CR-NET"},{"method_slug":"fast-yolov2","method_name":"Fast-YOLOv2"},{"method_slug":"yolov2","method_name":"YOLOv2"}],"datasets_introduced":[{"slug":"ufpr-alpr","name":"UFPR-ALPR","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/license-plate-recognition-on-ssig-segplate","task":"License Plate Recognition","dataset":"SSIG-SegPlate","model":"Fast-YOLOv2 + Fast-YOLOv2 + CR-NET","rank_in_archive_order":2,"of":2,"metrics":{"Rank-1 Recognition Rate":"85.45"},"uses_additional_data":false},{"leaderboard":"/sota/license-plate-recognition-on-ufpr-alpr","task":"License Plate Recognition","dataset":"UFPR-ALPR","model":"YOLOv2 + Fast-YOLOv2 + CR-NET","rank_in_archive_order":3,"of":3,"metrics":{"Rank-1 Recognition Rate":"64.89"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1802.09567","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}