Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models
Sign language is a unique communication tool helping to bridge the gap between people with hearing impairments and the general public. It holds paramount importance for various communities, as it allows individuals with hearing difficulties to communicate effectively. In sign languages, there are nu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2023/5195007 |
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| author | Qanita Bani Baker Nour Alqudah Tibra Alsmadi Rasha Awawdeh |
| author_facet | Qanita Bani Baker Nour Alqudah Tibra Alsmadi Rasha Awawdeh |
| author_sort | Qanita Bani Baker |
| collection | DOAJ |
| description | Sign language is a unique communication tool helping to bridge the gap between people with hearing impairments and the general public. It holds paramount importance for various communities, as it allows individuals with hearing difficulties to communicate effectively. In sign languages, there are numerous signs, each characterized by differences in hand shapes, hand positions, motions, facial expressions, and body parts used to convey specific meanings. The complexity of visual sign language recognition poses a significant challenge in the computer vision research area. This study presents an Arabic Sign Language recognition (ArSL) system that utilizes convolutional neural networks (CNNs) and several transfer learning models to automatically and accurately identify Arabic Sign Language characters. The dataset used for this study comprises 54,049 images of ArSL letters. The results of this research indicate that InceptionV3 outperformed other pretrained models, achieving a remarkable 100% accuracy score and a 0.00 loss score without overfitting. These impressive performance measures highlight the distinct capabilities of InceptionV3 in recognizing Arabic characters and underscore its robustness against overfitting. This enhances its potential for future research in the field of Arabic Sign Language recognition. |
| format | Article |
| id | doaj-art-85c6ea499a1f48b3a14b95acc64bc9e1 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-85c6ea499a1f48b3a14b95acc64bc9e12025-02-03T06:47:27ZengWileyApplied Computational Intelligence and Soft Computing1687-97322023-01-01202310.1155/2023/5195007Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning ModelsQanita Bani Baker0Nour Alqudah1Tibra Alsmadi2Rasha Awawdeh3Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceSign language is a unique communication tool helping to bridge the gap between people with hearing impairments and the general public. It holds paramount importance for various communities, as it allows individuals with hearing difficulties to communicate effectively. In sign languages, there are numerous signs, each characterized by differences in hand shapes, hand positions, motions, facial expressions, and body parts used to convey specific meanings. The complexity of visual sign language recognition poses a significant challenge in the computer vision research area. This study presents an Arabic Sign Language recognition (ArSL) system that utilizes convolutional neural networks (CNNs) and several transfer learning models to automatically and accurately identify Arabic Sign Language characters. The dataset used for this study comprises 54,049 images of ArSL letters. The results of this research indicate that InceptionV3 outperformed other pretrained models, achieving a remarkable 100% accuracy score and a 0.00 loss score without overfitting. These impressive performance measures highlight the distinct capabilities of InceptionV3 in recognizing Arabic characters and underscore its robustness against overfitting. This enhances its potential for future research in the field of Arabic Sign Language recognition.http://dx.doi.org/10.1155/2023/5195007 |
| spellingShingle | Qanita Bani Baker Nour Alqudah Tibra Alsmadi Rasha Awawdeh Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models Applied Computational Intelligence and Soft Computing |
| title | Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models |
| title_full | Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models |
| title_fullStr | Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models |
| title_full_unstemmed | Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models |
| title_short | Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models |
| title_sort | image based arabic sign language recognition system using transfer deep learning models |
| url | http://dx.doi.org/10.1155/2023/5195007 |
| work_keys_str_mv | AT qanitabanibaker imagebasedarabicsignlanguagerecognitionsystemusingtransferdeeplearningmodels AT nouralqudah imagebasedarabicsignlanguagerecognitionsystemusingtransferdeeplearningmodels AT tibraalsmadi imagebasedarabicsignlanguagerecognitionsystemusingtransferdeeplearningmodels AT rashaawawdeh imagebasedarabicsignlanguagerecognitionsystemusingtransferdeeplearningmodels |