Enhanced written arabic text recognition using deep learning techniques /
Mahmoud Mohamed Ahmed Badry
Enhanced written arabic text recognition using deep learning techniques / تحسين التعرف علي النص المكتوب باللغة العربية بإستخدام التعلم العميق Mahmoud Mohamed Ahmed Badry ; Supervised Hesham Hassan , Hussien Oakasha , Hanaa Bayomi - Cairo : Mahmoud Mohamed Ahmed Badry , 2018 - 56 Leaves : charts ; 30cm
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
Handwritten text recognition OCR Optical character recognition
Enhanced written arabic text recognition using deep learning techniques / تحسين التعرف علي النص المكتوب باللغة العربية بإستخدام التعلم العميق Mahmoud Mohamed Ahmed Badry ; Supervised Hesham Hassan , Hussien Oakasha , Hanaa Bayomi - Cairo : Mahmoud Mohamed Ahmed Badry , 2018 - 56 Leaves : charts ; 30cm
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
Handwritten text recognition OCR Optical character recognition