Application of soft computing techniques in machine reading of Quranic Kufic manuscripts
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...
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Springer
2022-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820303529 |
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| author | Aasim Zafar Arshad Iqbal |
| author_facet | Aasim Zafar Arshad Iqbal |
| author_sort | Aasim Zafar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-23c63593a40e4cb4820e9fda4cbc77ac |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-23c63593a40e4cb4820e9fda4cbc77ac2025-08-20T03:48:35ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-06-013463062306910.1016/j.jksuci.2020.04.017Application of soft computing techniques in machine reading of Quranic Kufic manuscriptsAasim Zafar0Arshad Iqbal1Department of Computer Science, Aligarh Muslim University, Aligarh, 202002 U.P., India; K.A. Nizami Centre for Quranic Studies, Aligarh Muslim University, Aligarh, 202002 U.P., IndiaCorresponding author.; Department of Computer Science, Aligarh Muslim University, Aligarh, 202002 U.P., India; K.A. Nizami Centre for Quranic Studies, Aligarh Muslim University, Aligarh, 202002 U.P., IndiaResearch 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.http://www.sciencedirect.com/science/article/pii/S1319157820303529Kufic scriptHistogram of Oriented Gradient (HOG)Local Binary Pattern (LBP)Support Vector Machine (SVM) |
| spellingShingle | Aasim Zafar Arshad Iqbal Application of soft computing techniques in machine reading of Quranic Kufic manuscripts Journal of King Saud University: Computer and Information Sciences Kufic script Histogram of Oriented Gradient (HOG) Local Binary Pattern (LBP) Support Vector Machine (SVM) |
| title | Application of soft computing techniques in machine reading of Quranic Kufic manuscripts |
| title_full | Application of soft computing techniques in machine reading of Quranic Kufic manuscripts |
| title_fullStr | Application of soft computing techniques in machine reading of Quranic Kufic manuscripts |
| title_full_unstemmed | Application of soft computing techniques in machine reading of Quranic Kufic manuscripts |
| title_short | Application of soft computing techniques in machine reading of Quranic Kufic manuscripts |
| title_sort | application of soft computing techniques in machine reading of quranic kufic manuscripts |
| topic | Kufic script Histogram of Oriented Gradient (HOG) Local Binary Pattern (LBP) Support Vector Machine (SVM) |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820303529 |
| work_keys_str_mv | AT aasimzafar applicationofsoftcomputingtechniquesinmachinereadingofquranickuficmanuscripts AT arshadiqbal applicationofsoftcomputingtechniquesinmachinereadingofquranickuficmanuscripts |