{"title":"Recognition of printed Urdu script","authors":"Zaheer Ahmad, Jehanzeb Khan Orakzai, Inam Shamsher","doi":"10.1109/ICDAR.2003.1227844","DOIUrl":null,"url":null,"abstract":"This paper deals with an Optical Character Recognitionsystem for printed Urdu, a popular Indian script. Thedevelopment of OCR for this script is difficult because (i) alarge number of characters have to be recognized (ii) thereare many similar shaped characters. In the proposedsystem individual characters are recognized using acombination of topological, contour and water reservoirconcept based features. The feature detection methods aresimple and robust. A prototype of the system has beentested on printed Urdu characters and currently achieves97.8% character level accuracy on average.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"113","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.2003.1227844","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 113
Abstract
This paper deals with an Optical Character Recognitionsystem for printed Urdu, a popular Indian script. Thedevelopment of OCR for this script is difficult because (i) alarge number of characters have to be recognized (ii) thereare many similar shaped characters. In the proposedsystem individual characters are recognized using acombination of topological, contour and water reservoirconcept based features. The feature detection methods aresimple and robust. A prototype of the system has beentested on printed Urdu characters and currently achieves97.8% character level accuracy on average.
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