[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-187262-en":3,"doc-seo-187262-105":30,"detail-sidebar-cat-1-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":11,"category_id":12,"category_name":13,"doc_title":14,"doc_description":15,"doc_content":16,"file_id":17,"file_url":18,"file_type":19,"file_size":20,"view_count":4,"is_deleted":4,"is_public":11,"is_downloadable":11,"audit_status":11,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":15,"update_tm":28,"read_time":29},187262,13056712833777,"Paura","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",1,158,"General","A Comprehensive Literature Review of Vehicle License Plate Detection Methods - Vehicle License Plate Detection Methods - A Comprehensive Literature Review","Vehicle license plate (LP) detection algorithms have advanced significantly in image-based recognition accuracy, yet performance is constrained by real-world environmental variability and the wide diversity of LP formats. Detecting multiple license plates in a single image under translation, scaling, rotation, and weather-related effects remains challenging, with only a limited number of methods performing reliably in unconstrained settings. This review systematically classifies LP detection approaches in the literature, analyzing their methodologies, strengths, and limitations to guide future research toward universal, robust LP detection and recognition systems.","| A Comprehensive Literature Review of Vehicle License Plate Detection Methods\u003Cbr>Narasimha Reddy Soora , Vinay Kumar Kotte* , Kumar Dorthi, Swathy Vodithala, Naliganti C S Kumar Department of Computer Science and Engineering, Kakatiya Institute of Technology and Science, Warangal 506015, India [Corresponding Author Email: kotte.vinaykumar@gmail.com](Corresponding Author Email: kotte.vinaykumar@gmail.com)\u003Cbr>|\n| --- |\n| Copyright: ©2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license\u003Cbr>([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). |\n\n\n| Received: 28 January 2023\u003Cbr>Revised: 23 November 2023\u003Cbr>Accepted: 16 February 2024\u003Cbr>Available online: 26 June 2024 | License plate (LP) detection algorithms have made considerable strides in the literature, showcasing enhanced performance in recognizing LPs from images. However, these algorithms face limitations from various environmental conditions and the diverse LP variants. Over several decades, researchers have diligently explored various approaches to LP detection. The task of detecting multiple LPs within an image while accommodating challenges like translation, scaling, rotation, and the influence of environmental and meteorological factors poses a formidable challenge, with only a select few algorithms proving effective. Efficient LP detection systems ideally mirror human perception, allowing the detection of multiple LPs within a given input image. Regrettably, most existing LP detection methods documented in the literature exhibit specificity towards particular vehicles or countries and perform optimally only under controlled conditions. This review paper systematically categorizes the LP detection methods found in the literature based on the techniques they employ for LP detection. It examines and analyzes their respective methodologies, strengths, and weaknesses. This comprehensive analysis aims to provide valuable insights for LP detection and recognition researchers. The ultimate goal is to inspire the development of universal LP detection methods capable of performing robustly under unconstrained real-world conditions. |\n| --- | --- |\n| Keywords:\u003Cbr>image processing, pattern recognition, license plate detection, license plate recognition, intelligent transport system, automatic number plate recognition |  |\n\n| Features Used to Detect LP | Data Set Information (Proprietary/Publicly Available Benchmark Data Set/Country/Types of Vehicles in the Data Set) | Data Set Size (No. of Images/Videos) | Accuracy | Processing\u003Cbr>Time |\n| --- | --- | --- | --- | --- |\n| Sobel operator for vertical edge detection, geometrical constraints, change in brightness | Proprietary data set, country: Japan, types of vehicles: cars | 100 images | - | - |\n| Hough transform, Sobel operator, contour algorithm | Proprietary data set, country: Vietnam, types of vehicles: cars, motorcycles | 805 images | 99.00% | 0.65 seconds |\n| Edge counts, geometrical properties | Proprietary data set, Country: Taiwan, types of vehicles: Motorcycles | 180 images | 98.00% | 0.075\u003Cbr>seconds |\n| Vertical Sobel mask, gradient accumulation | Publicly available data set, country: 15 nationalities, types of vehicles: cars | 365 images | 92.90% | - |\n| Vertical Sobel mask, gradient accumulation | Proprietary data set, country: European, types of vehicles: cars | 843 images | 82.00% | - |\n| Edge grouping, a width of LP | Proprietary data set, country: Korea, types of vehicles: cars | 250 images | 92.40% | - |\n| Canny edge detector, gradient information | Proprietary data set, country: Turkey, types of vehicles: cars | 259 images | 95.36% | - |\n| Prewitt edge detection, upper and lower edges of LP | Proprietary data set, country: China, types of vehicles: cars | - | 96.75% | 0.2 seconds |\n| HEM of Vertical Sobel operator | Proprietary data set, country: China, Pakistan, Serbia, Italy, USA, proprietary data set | 855 images | 90.4% | 0.2","cbCaiv2RGMacLnwj","https://ap.wps.com/l/cbCaiv2RGMacLnwj","pdf",3953032,13,"English","en",105,"# Abstract\n# Keywords\n# Features Used to Detect LP\n## Data Set Information and Performance Metrics\n## Traditional Vision-Based Techniques\n## Deep Learning Approaches","[{\"question\":\"What are the main challenges for license plate detection in real-world images?\",\"answer\":\"The review highlights issues from environmental conditions and diverse LP variants, including translation, scaling, rotation, and meteorological influences. These factors reduce effectiveness compared with controlled-condition performance.\"},{\"question\":\"How does the paper organize and evaluate license plate detection methods?\",\"answer\":\"It systematically categorizes LP detection methods by the techniques used, then examines their methodologies, strengths, and weaknesses. It also compares performance using dataset information, size, accuracy, and processing time.\"},{\"question\":\"Why is universal license plate detection still difficult to achieve?\",\"answer\":\"Most documented methods are specialized for particular vehicles or countries and work best under controlled conditions. The goal is to develop methods that remain robust across unconstrained real-world scenarios.\"}]","A Comprehensive Literature Review of Vehicle License Plate Detection Methods - Vehicle License Plate Detection Methods - A Comprehensive Literature Review | PDF",1788381819,5,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":14,"keywords":34,"description":15,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-comprehensive-literature-review-of-vehicle-license-plate-detection-methods-vehicle-license-plate-detection-methods-a-comprehensive-literature-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":11},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/template/","Template",2,{"item":49,"name":13,"@type":43,"position":50},"https://docshare.wps.com/template/general/",3,{"item":52,"name":14,"@type":43,"position":53},"https://docshare.wps.com/template/a-comprehensive-literature-review-of-vehicle-license-plate-detection-methods-vehicle-license-plate-detection-methods-a-comprehensive-literature-review/187262/",4,{"url":52,"name":14,"@type":55,"author":56,"headline":14,"publisher":58,"fileFormat":61,"inLanguage":23,"description":15,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-09-04","2026-09-02",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What are the main challenges for license plate detection in real-world images?","Question",{"text":76,"@type":77},"The review highlights issues from environmental conditions and diverse LP variants, including translation, scaling, rotation, and meteorological influences. These factors reduce effectiveness compared with controlled-condition performance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the paper organize and evaluate license plate detection methods?",{"text":81,"@type":77},"It systematically categorizes LP detection methods by the techniques used, then examines their methodologies, strengths, and weaknesses. It also compares performance using dataset information, size, accuracy, and processing time.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is universal license plate detection still difficult to achieve?",{"text":85,"@type":77},"Most documented methods are specialized for particular vehicles or countries and work best under controlled conditions. 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