{"title":"Recognizing Persian license plates in digital zoom condition","authors":"Elham Kordi Ghasrodashti, M. Yazdi","doi":"10.1109/ICETC.2010.5529561","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an algorithm of license plates recognition from their images captured by a camera in digital zoom using a binary time delay neural network (TDNN). Moreover, hard conditions such as the distance and angle variations as well as weather and light conditions are considered. For training the neural network, we collected the training images using the Zernike moment when the camera was not in the magnification state and the test images when the camera was in zooming state. The comparison was made between the proposed algorithm and the previous methods in character recognition like SVM and classical TDNN. The algorithms have been evaluated using 50 license plate images with magnification of 8. The recognition rate obtained by the proposed algorithm was 70%.","PeriodicalId":299461,"journal":{"name":"2010 2nd International Conference on Education Technology and Computer","volume":"105 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Conference on Education Technology and Computer","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETC.2010.5529561","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
In this paper, we propose an algorithm of license plates recognition from their images captured by a camera in digital zoom using a binary time delay neural network (TDNN). Moreover, hard conditions such as the distance and angle variations as well as weather and light conditions are considered. For training the neural network, we collected the training images using the Zernike moment when the camera was not in the magnification state and the test images when the camera was in zooming state. The comparison was made between the proposed algorithm and the previous methods in character recognition like SVM and classical TDNN. The algorithms have been evaluated using 50 license plate images with magnification of 8. The recognition rate obtained by the proposed algorithm was 70%.