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Deep Learning for Optical Character Recognition of Arabic Text

Rahmat, Mustaien Fathur Rahim (2020) Deep Learning for Optical Character Recognition of Arabic Text. IRC, Universiti Teknologi PETRONAS. (Submitted)

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Optical Character Recognition (OCR) of non-Latin scripts, such as Arabic script have been investigated over several decades ago. However, the recent advancement of deep learning has attracted many to improve existing solutions to OCR. Arabic writing system has its own distinctive style. Words are written from the right to the left. Features like ligatures, diacritics and vowel markings are commonly included in writing. Some characters may extend and overlap on top of another characters. This may cause difficulties when training models using conventional training method. Therefore, techniques chosen should be able recognize such features. Since Arabic calligraphy exists in many styles, this study will only focus on recognizing printed and written khat naskh. Using neural networks technology with the help of enormous and reliable datasets, models can be trained to get high accuracy and precision in recognizing the text. In this study, a hybrid neural network model will be built, which will concern on feature extraction and classification as to encounter difficulties in OCR of Arabic text.

Item Type: Final Year Project
Academic Subject : Academic Department - Information Communication Technology
Subject: Q Science > Q Science (General)
Divisions: Sciences and Information Technology > Computer and Information Sciences
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 23 Sep 2021 23:38
Last Modified: 23 Sep 2021 23:38
URI: http://utpedia.utp.edu.my/id/eprint/21779

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