{"title":"一种基于车牌自动识别的车辆盗窃及违章停车行为检测方法","authors":"A. Mohammad, M. Suneetha, M. Muqeet","doi":"10.1109/AISP53593.2022.9760556","DOIUrl":null,"url":null,"abstract":"A modern-day security technology is the Automatic Number Plate Recognition (ANPR) system. The fundamental component of an ANPR system is image processing. This uses an optical character recognition (OCR) approach to read and extract characters from a vehicle registration plate image. Automatic Number Plate Recognition (ANPR) has been popular in a variety of settings. It can be used by highway tollgate authorities to allow vehicles to enter toll roads by automatically recognising their license plates, providing them with a toll-slip, and then opening the road. Parking authorities in areas like malls and hotels use this technique to assign distinct parking spaces to individual cars and allow them to park in their designated area. We snap images of the license plate with this ANPR device, then process and extract every character of the license plate for exact detection. The crucial phase of ANPR is OCR, which extracts and converts the characters on the acquired image of the vehicle registration plate into text that can be decoded further. In this study, we propose a method for parking rule offenders that involves storing the extracted number plates in a database and cross-verifying them against existing registered number plates that have paid the parking fee. Our proposed method can also detect stolen autos.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"5 2 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Efficient Method for Vehicle theft and Parking rule Violators Detection using Automatic Number Plate Recognition\",\"authors\":\"A. Mohammad, M. Suneetha, M. Muqeet\",\"doi\":\"10.1109/AISP53593.2022.9760556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A modern-day security technology is the Automatic Number Plate Recognition (ANPR) system. The fundamental component of an ANPR system is image processing. This uses an optical character recognition (OCR) approach to read and extract characters from a vehicle registration plate image. Automatic Number Plate Recognition (ANPR) has been popular in a variety of settings. It can be used by highway tollgate authorities to allow vehicles to enter toll roads by automatically recognising their license plates, providing them with a toll-slip, and then opening the road. Parking authorities in areas like malls and hotels use this technique to assign distinct parking spaces to individual cars and allow them to park in their designated area. We snap images of the license plate with this ANPR device, then process and extract every character of the license plate for exact detection. The crucial phase of ANPR is OCR, which extracts and converts the characters on the acquired image of the vehicle registration plate into text that can be decoded further. In this study, we propose a method for parking rule offenders that involves storing the extracted number plates in a database and cross-verifying them against existing registered number plates that have paid the parking fee. Our proposed method can also detect stolen autos.\",\"PeriodicalId\":6793,\"journal\":{\"name\":\"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)\",\"volume\":\"5 2 1\",\"pages\":\"1-4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AISP53593.2022.9760556\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AISP53593.2022.9760556","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Efficient Method for Vehicle theft and Parking rule Violators Detection using Automatic Number Plate Recognition
A modern-day security technology is the Automatic Number Plate Recognition (ANPR) system. The fundamental component of an ANPR system is image processing. This uses an optical character recognition (OCR) approach to read and extract characters from a vehicle registration plate image. Automatic Number Plate Recognition (ANPR) has been popular in a variety of settings. It can be used by highway tollgate authorities to allow vehicles to enter toll roads by automatically recognising their license plates, providing them with a toll-slip, and then opening the road. Parking authorities in areas like malls and hotels use this technique to assign distinct parking spaces to individual cars and allow them to park in their designated area. We snap images of the license plate with this ANPR device, then process and extract every character of the license plate for exact detection. The crucial phase of ANPR is OCR, which extracts and converts the characters on the acquired image of the vehicle registration plate into text that can be decoded further. In this study, we propose a method for parking rule offenders that involves storing the extracted number plates in a database and cross-verifying them against existing registered number plates that have paid the parking fee. Our proposed method can also detect stolen autos.