{"title":"License Plate Recognition System Based on Artificial Intelligence with Different Approach","authors":"Aslı Göde, Ahmet Doğan","doi":"10.31202/ecjse.1172426","DOIUrl":null,"url":null,"abstract":"An license plate recognition system (LPRS) generally provides control and security. These systems are created using methods such as artificial intelligence, machine learning, artificial neural networks (ANN), deep learning, fuzzy logic, expert systems, and image processing. This study aims to create an LPRS using artificial intelligence and image processing techniques. The prepared system is for rectangular-sized plates. An LPRS consists of 3 main stages. The first stage is to detect the plate region. At this stage, converting to grayscale, bilateral filtering, canny filtering, and contour were applied to vehicle images. The second stage is to crop the plate region. In the second stage, the masking method was employed. The pytesseract algorithm was used to recognize license plate characters in the last stage. To create the system, Raspberry Pi 4 Single-Board Computer (SBC) was used for hardware; python programming language was utilized for software. The results showed that the system worked successfully at the rate of 100% in the first two stages and at the rate of 91.82% in the last stage. The results suggest that the system works successfully.","PeriodicalId":11622,"journal":{"name":"El-Cezeri Fen ve Mühendislik Dergisi","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"El-Cezeri Fen ve Mühendislik Dergisi","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31202/ecjse.1172426","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
An license plate recognition system (LPRS) generally provides control and security. These systems are created using methods such as artificial intelligence, machine learning, artificial neural networks (ANN), deep learning, fuzzy logic, expert systems, and image processing. This study aims to create an LPRS using artificial intelligence and image processing techniques. The prepared system is for rectangular-sized plates. An LPRS consists of 3 main stages. The first stage is to detect the plate region. At this stage, converting to grayscale, bilateral filtering, canny filtering, and contour were applied to vehicle images. The second stage is to crop the plate region. In the second stage, the masking method was employed. The pytesseract algorithm was used to recognize license plate characters in the last stage. To create the system, Raspberry Pi 4 Single-Board Computer (SBC) was used for hardware; python programming language was utilized for software. The results showed that the system worked successfully at the rate of 100% in the first two stages and at the rate of 91.82% in the last stage. The results suggest that the system works successfully.
车牌识别系统(LPRS)通常提供控制和安全。这些系统是使用人工智能、机器学习、人工神经网络(ANN)、深度学习、模糊逻辑、专家系统和图像处理等方法创建的。本研究旨在利用人工智能和图像处理技术创建一个LPRS。所制备的系统适用于矩形大小的板。LPRS包括3个主要阶段。第一步是检测板块区域。该阶段对车辆图像进行灰度转换、双边滤波、canny滤波和轮廓化处理。第二阶段是裁切印版区域。在第二阶段,采用掩蔽法。最后,采用pytesseract算法对车牌字符进行识别。为了创建这个系统,硬件使用了Raspberry Pi 4单板计算机(SBC);软件采用Python编程语言。结果表明,该系统前两段的萃取率为100%,最后一段的萃取率为91.82%。结果表明,该系统运行良好。