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Layout analysis of arabic documents / Amany Mohamed Hesham Farouk ; Supervised Ibrahim Farag , Amr Badr , Sherif Abdou

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Amany Mohamed Hesham Farouk , 2017Description: 78 Leaves : charts , facsimiles ; 30cmOther title:
  • تحليل التنسيق في الوثائق العربية [Added title page title]
Subject(s): Online resources: Available additional physical forms:
  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science Summary: Document layout analysis is important in converting document images into text. Arabic script cursive nature and different writing styles cause challenges. In this work, we introduce an approach for segmenting image into zones. Text zones are segmented into lines and then words. System accuracy achieved is 93.2% for zone classification and 98.3% for line segmentation. Also, a posteriori, word based and font-size invariant approach for font recognition using textural features based on cosine transform is proposed. Results show that the average recognition rate is 93.2%
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.03.M.Sc.2017.Am.L (Browse shelf(Opens below)) Not for loan 01010110073775000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.03.M.Sc.2017.Am.L (Browse shelf(Opens below)) 73775.CD Not for loan 01020110073775000

Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science

Document layout analysis is important in converting document images into text. Arabic script cursive nature and different writing styles cause challenges. In this work, we introduce an approach for segmenting image into zones. Text zones are segmented into lines and then words. System accuracy achieved is 93.2% for zone classification and 98.3% for line segmentation. Also, a posteriori, word based and font-size invariant approach for font recognition using textural features based on cosine transform is proposed. Results show that the average recognition rate is 93.2%

Issued also as CD

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