{"title":"一种改进的细化方法以处理带有噪声轮廓的字符","authors":"Soumyadeep Ghosh, Soumen Bag","doi":"10.1109/NCVPRIPG.2013.6776178","DOIUrl":null,"url":null,"abstract":"Thinning is an important preprocessing operation used in different document image processing and analysis applications. The main objective of thinning is to obtain single-pixel thin skeleton without any shape distortion. It is noticed that documents written in ink-sketch pens and scanned with high precision scanners suffer from high degree of unevenness on their outer surfaces. This unevenness results in severe distortions in the shapes of thinned images, which makes them unsuitable for efficient recognition. These distortions are mainly two types namely, spurious loops and spurious strokes. Our proposed algorithm gets rid of these distortions in the thinned image. We have tested our approach on our own data set of about 1500 characters and have got promising results.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An improvement on thinning to handle characters with noisy contour\",\"authors\":\"Soumyadeep Ghosh, Soumen Bag\",\"doi\":\"10.1109/NCVPRIPG.2013.6776178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Thinning is an important preprocessing operation used in different document image processing and analysis applications. The main objective of thinning is to obtain single-pixel thin skeleton without any shape distortion. It is noticed that documents written in ink-sketch pens and scanned with high precision scanners suffer from high degree of unevenness on their outer surfaces. This unevenness results in severe distortions in the shapes of thinned images, which makes them unsuitable for efficient recognition. These distortions are mainly two types namely, spurious loops and spurious strokes. Our proposed algorithm gets rid of these distortions in the thinned image. We have tested our approach on our own data set of about 1500 characters and have got promising results.\",\"PeriodicalId\":436402,\"journal\":{\"name\":\"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NCVPRIPG.2013.6776178\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCVPRIPG.2013.6776178","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An improvement on thinning to handle characters with noisy contour
Thinning is an important preprocessing operation used in different document image processing and analysis applications. The main objective of thinning is to obtain single-pixel thin skeleton without any shape distortion. It is noticed that documents written in ink-sketch pens and scanned with high precision scanners suffer from high degree of unevenness on their outer surfaces. This unevenness results in severe distortions in the shapes of thinned images, which makes them unsuitable for efficient recognition. These distortions are mainly two types namely, spurious loops and spurious strokes. Our proposed algorithm gets rid of these distortions in the thinned image. We have tested our approach on our own data set of about 1500 characters and have got promising results.