Arabic Sign Language Recognition System for Alphabets Using Machine Learning Techniques
In recent years, the role of pattern recognition in systems based on human computer interaction (HCI) has spread in terms of computer vision applications and machine learning, and one of the most important of these applications is to recognize the hand gestures used in dealing with deaf people, in p...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/2995851 |
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| author | Gamal Tharwat Abdelmoty M. Ahmed Belgacem Bouallegue |
| author_facet | Gamal Tharwat Abdelmoty M. Ahmed Belgacem Bouallegue |
| author_sort | Gamal Tharwat |
| collection | DOAJ |
| description | In recent years, the role of pattern recognition in systems based on human computer interaction (HCI) has spread in terms of computer vision applications and machine learning, and one of the most important of these applications is to recognize the hand gestures used in dealing with deaf people, in particular to recognize the dashed letters in surahs of the Quran. In this paper, we suggest an Arabic Alphabet Sign Language Recognition System (AArSLRS) using the vision-based approach. The proposed system consists of four stages: the stage of data processing, preprocessing of data, feature extraction, and classification. The system deals with three types of datasets: data dealing with bare hands and a dark background, data dealing with bare hands, but with a light background, and data dealing with hands wearing dark colored gloves. AArSLRS begins with obtaining an image of the alphabet gestures, then revealing the hand from the image and isolating it from the background using one of the proposed methods, after which the hand features are extracted according to the selection method used to extract them. Regarding the classification process in this system, we have used supervised learning techniques for the classification of 28-letter Arabic alphabet using 9240 images. We focused on the classification for 14 alphabetic letters that represent the first Quran surahs in the Quranic sign language (QSL). AArSLRS achieved an accuracy of 99.5% for the K-Nearest Neighbor (KNN) classifier. |
| format | Article |
| id | doaj-art-e81428d5acd448d28a7e3b173872ae23 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-e81428d5acd448d28a7e3b173872ae232025-02-03T07:23:29ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552021-01-01202110.1155/2021/29958512995851Arabic Sign Language Recognition System for Alphabets Using Machine Learning TechniquesGamal Tharwat0Abdelmoty M. Ahmed1Belgacem Bouallegue2Department of Computers and Systems Engineering, Faculty of Engineering, Al-Azhar University, Cairo, EgyptDepartment of Computers and Systems Engineering, Faculty of Engineering, Al-Azhar University, Cairo, EgyptDepartment of Computer Engineering, King Khalid University, Abha, Saudi ArabiaIn recent years, the role of pattern recognition in systems based on human computer interaction (HCI) has spread in terms of computer vision applications and machine learning, and one of the most important of these applications is to recognize the hand gestures used in dealing with deaf people, in particular to recognize the dashed letters in surahs of the Quran. In this paper, we suggest an Arabic Alphabet Sign Language Recognition System (AArSLRS) using the vision-based approach. The proposed system consists of four stages: the stage of data processing, preprocessing of data, feature extraction, and classification. The system deals with three types of datasets: data dealing with bare hands and a dark background, data dealing with bare hands, but with a light background, and data dealing with hands wearing dark colored gloves. AArSLRS begins with obtaining an image of the alphabet gestures, then revealing the hand from the image and isolating it from the background using one of the proposed methods, after which the hand features are extracted according to the selection method used to extract them. Regarding the classification process in this system, we have used supervised learning techniques for the classification of 28-letter Arabic alphabet using 9240 images. We focused on the classification for 14 alphabetic letters that represent the first Quran surahs in the Quranic sign language (QSL). AArSLRS achieved an accuracy of 99.5% for the K-Nearest Neighbor (KNN) classifier.http://dx.doi.org/10.1155/2021/2995851 |
| spellingShingle | Gamal Tharwat Abdelmoty M. Ahmed Belgacem Bouallegue Arabic Sign Language Recognition System for Alphabets Using Machine Learning Techniques Journal of Electrical and Computer Engineering |
| title | Arabic Sign Language Recognition System for Alphabets Using Machine Learning Techniques |
| title_full | Arabic Sign Language Recognition System for Alphabets Using Machine Learning Techniques |
| title_fullStr | Arabic Sign Language Recognition System for Alphabets Using Machine Learning Techniques |
| title_full_unstemmed | Arabic Sign Language Recognition System for Alphabets Using Machine Learning Techniques |
| title_short | Arabic Sign Language Recognition System for Alphabets Using Machine Learning Techniques |
| title_sort | arabic sign language recognition system for alphabets using machine learning techniques |
| url | http://dx.doi.org/10.1155/2021/2995851 |
| work_keys_str_mv | AT gamaltharwat arabicsignlanguagerecognitionsystemforalphabetsusingmachinelearningtechniques AT abdelmotymahmed arabicsignlanguagerecognitionsystemforalphabetsusingmachinelearningtechniques AT belgacembouallegue arabicsignlanguagerecognitionsystemforalphabetsusingmachinelearningtechniques |