Subword Recognition in Historical Arabic Documents using C-GRUs

Hassen, Hanadi, Al-Madeed, Somaya and Bouridane, Ahmed (2021) Subword Recognition in Historical Arabic Documents using C-GRUs. TEM Journal, 10 (4). pp. 1630-1637. ISSN 2217-8309

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The recent years have witnessed an increased tendency to digitize historical manuscripts that not only ensures the preservation of these collections but also allows researchers and end-users’ direct access to these images. Recognition of Arabic handwriting is challenging due to the highly cursive nature of the script and other challenges associated with historical documents (degradation etc.). This paper presents an end-to-end system to recognize Arabic handwritten sub words in historical documents. More specifically, we introduce a hybrid CNN-GRU model where the shallow convolutional network learns robust feature representations while the GRU layers carry out the sequence modelling and generate the transcription of the text. The proposed system is evaluated on two different datasets, IBN SINA and VML-HD reporting recognition rates of 96.10% and 98.60% respectively. A comparison with existing techniques evaluated on the same datasets validates the effectiveness of our proposed model in characterizing Arabic subwords.

Item Type: Article
Additional Information: Funding information: This publication was made by NPRP grant # NPRP 11S – 0113 – 180276 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Uncontrolled Keywords: handwriting recognition, Arabic historical documents, CNNs, GRUs, classification
Subjects: G400 Computer Science
G500 Information Systems
X900 Others in Education
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 02 Feb 2022 10:22
Last Modified: 02 Feb 2022 10:30

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