Mustafa Qahtan Alsudani, Safa Riyadh Waheed, K. A. Kadhim, M. M. Adnan, Ameer Al-khaykan
{"title":"Automatic Car Number Plate Detection using Morphological Image Processing","authors":"Mustafa Qahtan Alsudani, Safa Riyadh Waheed, K. A. Kadhim, M. M. Adnan, Ameer Al-khaykan","doi":"10.11113/mjfas.v19n3.2910","DOIUrl":null,"url":null,"abstract":"One of the most common uses of computer vision, automatic number plate recognition (ANPR) is also a pretty well-explored subject with numerous effective solutions. Due to regional differences in license plate design, however, these solutions are often optimized for a specific setting. Number plate recognition algorithms are often dependent on these aspects, making a universal solution unlikely due to the fact that the image analysis methods used to develop these algorithms cannot guarantee a perfect success rate. In this research, we offer an algorithm tailor-made for use with brand-new license plates in Iraq. The method employs edge detection, Feature Detection, and mathematical morphology to find the plate; it was developed in C++ using the OpenCV library. When characters were found on the plate, they were entered into the Easy OCR engine for analysis.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"112 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Malaysian Journal of Fundamental and Applied Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11113/mjfas.v19n3.2910","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
One of the most common uses of computer vision, automatic number plate recognition (ANPR) is also a pretty well-explored subject with numerous effective solutions. Due to regional differences in license plate design, however, these solutions are often optimized for a specific setting. Number plate recognition algorithms are often dependent on these aspects, making a universal solution unlikely due to the fact that the image analysis methods used to develop these algorithms cannot guarantee a perfect success rate. In this research, we offer an algorithm tailor-made for use with brand-new license plates in Iraq. The method employs edge detection, Feature Detection, and mathematical morphology to find the plate; it was developed in C++ using the OpenCV library. When characters were found on the plate, they were entered into the Easy OCR engine for analysis.