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Application of soft computing techniques in machine reading of Quranic Kufic manuscripts

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Title: Application of soft computing techniques in machine reading of Quranic Kufic manuscripts
Authors: Aasim Zafar, Arshad Iqbal
Source: Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 6, Pp 3062-3069 (2022)
Publisher Information: Elsevier, 2022.
Publication Year: 2022
Collection: LCC:Electronic computers. Computer science
Subject Terms: Kufic script, Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP), Support Vector Machine (SVM), Electronic computers. Computer science, QA75.5-76.95
More Details: Research work in the field of offline Arabic handwriting recognition has seen an exponential growth during past decades. Despite much work dedicated to the recognition of handwritten Arabic text, there remains a lot to achieve in this regard. Though there exists a lot of work that is confined to the Arabic text, this paper presents an approach towards classifying and recognizing text written in one of the famous scripts of Arabic language i.e. Kufic script. The approach based on character segmentation does not perform well in recognizing Kufic text due to various complexities. The proposed system is based on word segmentation and employs a Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) feature extraction techniques. Later in the paper a comparison is given between the numerical results of proposed technique with previous Arabic text recognition techniques to show the effectiveness of present work. This approach yields effective results with 97.05% accuracy in recognizing the Arabic text written in Kufic script using Polynomial kernel of SVM classifier. Experimental results show that the proposed system for recognition of Kufic script performs better than the previous recognition systems for Arabic text.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1319-1578
Relation: http://www.sciencedirect.com/science/article/pii/S1319157820303529; https://doaj.org/toc/1319-1578
DOI: 10.1016/j.jksuci.2020.04.017
Access URL: https://doaj.org/article/23c63593a40e4cb4820e9fda4cbc77ac
Accession Number: edsdoj.23c63593a40e4cb4820e9fda4cbc77ac
Database: Directory of Open Access Journals
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  Data: Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 6, Pp 3062-3069 (2022)
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  Data: Research work in the field of offline Arabic handwriting recognition has seen an exponential growth during past decades. Despite much work dedicated to the recognition of handwritten Arabic text, there remains a lot to achieve in this regard. Though there exists a lot of work that is confined to the Arabic text, this paper presents an approach towards classifying and recognizing text written in one of the famous scripts of Arabic language i.e. Kufic script. The approach based on character segmentation does not perform well in recognizing Kufic text due to various complexities. The proposed system is based on word segmentation and employs a Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) feature extraction techniques. Later in the paper a comparison is given between the numerical results of proposed technique with previous Arabic text recognition techniques to show the effectiveness of present work. This approach yields effective results with 97.05% accuracy in recognizing the Arabic text written in Kufic script using Polynomial kernel of SVM classifier. Experimental results show that the proposed system for recognition of Kufic script performs better than the previous recognition systems for Arabic text.
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