| 000 | 01806cam a2200349 a 4500 | ||
|---|---|---|---|
| 003 | EG-GiCUC | ||
| 005 | 20250223031857.0 | ||
| 008 | 171216s2017 ua dh f m 000 0 eng d | ||
| 040 |
_aEG-GiCUC _beng _cEG-GiCUC |
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| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aM.Sc | ||
| 099 | _aCai01.20.03.M.Sc.2017.Am.L | ||
| 100 | 0 | _aAmany Mohamed Hesham Farouk | |
| 245 | 1 | 0 |
_aLayout analysis of arabic documents / _cAmany Mohamed Hesham Farouk ; Supervised Ibrahim Farag , Amr Badr , Sherif Abdou |
| 246 | 1 | 5 | _aتحليل التنسيق في الوثائق العربية |
| 260 |
_aCairo : _bAmany Mohamed Hesham Farouk , _c2017 |
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| 300 |
_a78 Leaves : _bcharts , facsimiles ; _c30cm |
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| 502 | _aThesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science | ||
| 520 | _aDocument 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% | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aAdaptive binarization | |
| 653 | 4 | _aArabic documents | |
| 653 | 4 | _aLayout analysis | |
| 700 | 0 |
_aAmr Badr , _eSupervisor |
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| 700 | 0 |
_aIbrahim Farag , _eSupervisor |
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| 700 | 0 |
_aSherif Abdou , _eSupervisor |
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| 856 | _uhttp://172.23.153.220/th.pdf | ||
| 905 |
_aNazla _eRevisor |
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| 905 |
_aShimaa _eCataloger |
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| 942 |
_2ddc _cTH |
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| 999 |
_c64031 _d64031 |
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