{"title":"用于性能表征的线条图退化模型","authors":"Jian Zhai, Wenyin Liu, D. Dori, Qing Li","doi":"10.1109/ICDAR.2003.1227813","DOIUrl":null,"url":null,"abstract":"Line detection algorithms constitute the basis fortechnical document analysis and recognition. Theperformance of these algorithms decreases as the qualityof the documents degrades. To test the robustness of linedetection algorithms under noisy circumstance, wepropose a document degradation mode, which simulatesnoise types that drawings may undergo during theirproduction, storage, photocopying, or scanning. Using ourmodel, a series of document images at various noise levelsand types can be generated for testing the performance ofline detection algorithms. To illustrate that our model isconsistent with real world noise types, we validated themethod by applying it to three line recognition algorithms.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"A line drawings degradation model for performance characterization\",\"authors\":\"Jian Zhai, Wenyin Liu, D. Dori, Qing Li\",\"doi\":\"10.1109/ICDAR.2003.1227813\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Line detection algorithms constitute the basis fortechnical document analysis and recognition. Theperformance of these algorithms decreases as the qualityof the documents degrades. To test the robustness of linedetection algorithms under noisy circumstance, wepropose a document degradation mode, which simulatesnoise types that drawings may undergo during theirproduction, storage, photocopying, or scanning. Using ourmodel, a series of document images at various noise levelsand types can be generated for testing the performance ofline detection algorithms. To illustrate that our model isconsistent with real world noise types, we validated themethod by applying it to three line recognition algorithms.\",\"PeriodicalId\":249193,\"journal\":{\"name\":\"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-08-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDAR.2003.1227813\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2003.1227813","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A line drawings degradation model for performance characterization
Line detection algorithms constitute the basis fortechnical document analysis and recognition. Theperformance of these algorithms decreases as the qualityof the documents degrades. To test the robustness of linedetection algorithms under noisy circumstance, wepropose a document degradation mode, which simulatesnoise types that drawings may undergo during theirproduction, storage, photocopying, or scanning. Using ourmodel, a series of document images at various noise levelsand types can be generated for testing the performance ofline detection algorithms. To illustrate that our model isconsistent with real world noise types, we validated themethod by applying it to three line recognition algorithms.