Enhanced written arabic text recognition using deep learning techniques / Mahmoud Mohamed Ahmed Badry ; Supervised Hesham Hassan , Hussien Oakasha , Hanaa Bayomi

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Mahmoud Mohamed Ahmed Badry

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TextLanguage: English Publication details: Cairo : Mahmoud Mohamed Ahmed Badry , 2018Description: 56 Leaves : charts ; 30cmOther title:

تحسين التعرف علي النص المكتوب باللغة العربية بإستخدام التعلم العميق [Added title page title]

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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Computer and Information - Department of Computer Science Summary: Recognizing text in images has many useful applications, which include document archiving and searching. Improving the accuracy of Arabic text recognition in imagery requires a big modern dataset for machine-learning models learning. This thesis proposes a new dataset, called QTID, for Quran Text Image Dataset, the first Arabic dataset that includes Arabic diacritics. Experimental evaluation shows that current best Arabic text recognition engines cannot work well with word images from the proposed dataset. Two deep learning models was proposed that learned using QTID. Comparing these models outputs to current best Arabic text recognition engines shows that their accuracy outperforms these engines

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Item type	Current library	Home library	Call number	Copy number	Status	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.20.03.M.Sc.2018.Ma.E (Browse shelf(Opens below))		Not for loan	01010110078016000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.20.03.M.Sc.2018.Ma.E (Browse shelf(Opens below))	78016.CD	Not for loan	01020110078016000

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

Recognizing text in images has many useful applications, which include document archiving and searching. Improving the accuracy of Arabic text recognition in imagery requires a big modern dataset for machine-learning models learning. This thesis proposes a new dataset, called QTID, for Quran Text Image Dataset, the first Arabic dataset that includes Arabic diacritics. Experimental evaluation shows that current best Arabic text recognition engines cannot work well with word images from the proposed dataset. Two deep learning models was proposed that learned using QTID. Comparing these models outputs to current best Arabic text recognition engines shows that their accuracy outperforms these engines

Issued also as CD

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