R. Lins, E. Kavallieratou, E. B. Smith, R. Bernardino, D. Jesus
{"title":"ICDAR 2019 Time-Quality Binarization Competition","authors":"R. Lins, E. Kavallieratou, E. B. Smith, R. Bernardino, D. Jesus","doi":"10.1109/ICDAR.2019.00248","DOIUrl":null,"url":null,"abstract":"The ICDAR 2019 Time-Quality Binarization Competition assessed the performance of seventeen new together with thirty previously published binarization algorithms. The quality of the resulting two-tone image and the execution time were assessed. Comparisons were on both in \"real-world\" and synthetic scanned images, and in documents photographed with four models of widely used portable phones. Most of the submitted algorithms employed machine learning techniques and performed best on the most complex images. Traditional algorithms provided very good results at a fraction of the time.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Document Analysis and Recognition (ICDAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2019.00248","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
The ICDAR 2019 Time-Quality Binarization Competition assessed the performance of seventeen new together with thirty previously published binarization algorithms. The quality of the resulting two-tone image and the execution time were assessed. Comparisons were on both in "real-world" and synthetic scanned images, and in documents photographed with four models of widely used portable phones. Most of the submitted algorithms employed machine learning techniques and performed best on the most complex images. Traditional algorithms provided very good results at a fraction of the time.