{"title":"Automatic Recognition Algorithm of Quick Response Code Based on Embedded System","authors":"Yue Liu, Mingjun Liu","doi":"10.1109/ISDA.2006.253712","DOIUrl":null,"url":null,"abstract":"The automatic recognition algorithm of quick response code is discussed in this paper. An image processing system based on embedded system is described to be able to binarization, location, segment, and decoding the QR code. In order to adapting various sizes, various gray-level values, and under various lighting conditions of real bar code image, a high-speed, high-accuracy binarization method is developed, which can locate the finder pattern accurately and integrate the local thresholding method with global thresholding. Experiments have shown that over 99% barcode can be optimally recognized with the proposed algorithm. It can achieve higher recognition rate of high density bar code, and is applicable to real world scene image","PeriodicalId":116729,"journal":{"name":"Sixth International Conference on Intelligent Systems Design and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"44","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sixth International Conference on Intelligent Systems Design and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISDA.2006.253712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 44
Abstract
The automatic recognition algorithm of quick response code is discussed in this paper. An image processing system based on embedded system is described to be able to binarization, location, segment, and decoding the QR code. In order to adapting various sizes, various gray-level values, and under various lighting conditions of real bar code image, a high-speed, high-accuracy binarization method is developed, which can locate the finder pattern accurately and integrate the local thresholding method with global thresholding. Experiments have shown that over 99% barcode can be optimally recognized with the proposed algorithm. It can achieve higher recognition rate of high density bar code, and is applicable to real world scene image