Liuan Wang, Wei-liang Fan, Yuan He, Jun Sun, Yutaka Katsuyama, Y. Hotta
{"title":"Text detection in natural scene images with user-intention","authors":"Liuan Wang, Wei-liang Fan, Yuan He, Jun Sun, Yutaka Katsuyama, Y. Hotta","doi":"10.1109/ICPR.2014.503","DOIUrl":null,"url":null,"abstract":"We propose an accurate and robust coarse-to-fine text detection scheme with user-intention which captures the intrinsic characteristics of natural scene texts. In the coarse detection stage, a double edge detector is designed to estimate the symmetry of stroke and the stroke width, which help segment the foreground. Then the initial user-intention region is extended to generate a coarse bounding box based on the estimated foreground. In the refinement stage, candidate connected components (CCs) from Niblack decomposition, are grouped together by location to form text lines after noise removal and layer selection. Experimental results demonstrate the effectiveness of the proposed method which yields higher performance compared with state-of-the-art methods.","PeriodicalId":388385,"journal":{"name":"2013 IEEE International Conference on Image Processing","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2014.503","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
We propose an accurate and robust coarse-to-fine text detection scheme with user-intention which captures the intrinsic characteristics of natural scene texts. In the coarse detection stage, a double edge detector is designed to estimate the symmetry of stroke and the stroke width, which help segment the foreground. Then the initial user-intention region is extended to generate a coarse bounding box based on the estimated foreground. In the refinement stage, candidate connected components (CCs) from Niblack decomposition, are grouped together by location to form text lines after noise removal and layer selection. Experimental results demonstrate the effectiveness of the proposed method which yields higher performance compared with state-of-the-art methods.