Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model
<p dir="ltr">Every one of us has a unique manner of communicating to explore the world, and such communication helps to interpret life. Sign language is the popular language of communication for hearing and speech-disabled people. When a sign language user interacts with a non-sign l...
محفوظ في:
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| مؤلفون آخرون: | , , , , , , , , |
| منشور في: |
2023
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إضافة وسم
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| _version_ | 1864513507574153216 |
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| author | Kanchon Kanti Podder (19459564) |
| author2 | Maymouna Ezeddin (16891368) Muhammad E. H. Chowdhury (14150526) Md. Shaheenur Islam Sumon (17983810) Anas M. Tahir (16870077) Mohamed Arselene Ayari (16869978) Proma Dutta (19459567) Amith Khandakar (14151981) Zaid Bin Mahbub (16869975) Muhammad Abdul Kadir (16869963) |
| author2_role | author author author author author author author author author |
| author_facet | Kanchon Kanti Podder (19459564) Maymouna Ezeddin (16891368) Muhammad E. H. Chowdhury (14150526) Md. Shaheenur Islam Sumon (17983810) Anas M. Tahir (16870077) Mohamed Arselene Ayari (16869978) Proma Dutta (19459567) Amith Khandakar (14151981) Zaid Bin Mahbub (16869975) Muhammad Abdul Kadir (16869963) |
| author_role | author |
| dc.creator.none.fl_str_mv | Kanchon Kanti Podder (19459564) Maymouna Ezeddin (16891368) Muhammad E. H. Chowdhury (14150526) Md. Shaheenur Islam Sumon (17983810) Anas M. Tahir (16870077) Mohamed Arselene Ayari (16869978) Proma Dutta (19459567) Amith Khandakar (14151981) Zaid Bin Mahbub (16869975) Muhammad Abdul Kadir (16869963) |
| dc.date.none.fl_str_mv | 2023-08-14T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s23167156 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Signer-Independent_Arabic_Sign_Language_Recognition_System_Using_Deep_Learning_Model/26798149 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Human-centred computing Arabic Sign Language deep learning dynamic sign language segmentation MediaPipe |
| dc.title.none.fl_str_mv | Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Every one of us has a unique manner of communicating to explore the world, and such communication helps to interpret life. Sign language is the popular language of communication for hearing and speech-disabled people. When a sign language user interacts with a non-sign language user, it becomes difficult for a signer to express themselves to another person. A sign language recognition system can help a signer to interpret the sign of a non-sign language user. This study presents a sign language recognition system that is capable of recognizing Arabic Sign Language from recorded RGB videos. To achieve this, two datasets were considered, such as (1) the raw dataset and (2) the face–hand region-based segmented dataset produced from the raw dataset. Moreover, operational layer-based multi-layer perceptron “SelfMLP” is proposed in this study to build CNN-LSTM-SelfMLP models for Arabic Sign Language recognition. MobileNetV2 and ResNet18-based CNN backbones and three SelfMLPs were used to construct six different models of CNN-LSTM-SelfMLP architecture for performance comparison of Arabic Sign Language recognition. This study examined the signer-independent mode to deal with real-time application circumstances. As a result, MobileNetV2-LSTM-SelfMLP on the segmented dataset achieved the best accuracy of 87.69% with 88.57% precision, 87.69% recall, 87.72% F1 score, and 99.75% specificity. Overall, face–hand region-based segmentation and SelfMLP-infused MobileNetV2-LSTM-SelfMLP surpassed the previous findings on Arabic Sign Language recognition by 10.970% accuracy.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s23167156" target="_blank">https://dx.doi.org/10.3390/s23167156</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0a77a7f167f6f328ae8438c1083f8c36 |
| identifier_str_mv | 10.3390/s23167156 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26798149 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Signer-Independent Arabic Sign Language Recognition System Using Deep Learning ModelKanchon Kanti Podder (19459564)Maymouna Ezeddin (16891368)Muhammad E. H. Chowdhury (14150526)Md. Shaheenur Islam Sumon (17983810)Anas M. Tahir (16870077)Mohamed Arselene Ayari (16869978)Proma Dutta (19459567)Amith Khandakar (14151981)Zaid Bin Mahbub (16869975)Muhammad Abdul Kadir (16869963)Information and computing sciencesArtificial intelligenceComputer vision and multimedia computationHuman-centred computingArabic Sign Languagedeep learningdynamic sign languagesegmentationMediaPipe<p dir="ltr">Every one of us has a unique manner of communicating to explore the world, and such communication helps to interpret life. Sign language is the popular language of communication for hearing and speech-disabled people. When a sign language user interacts with a non-sign language user, it becomes difficult for a signer to express themselves to another person. A sign language recognition system can help a signer to interpret the sign of a non-sign language user. This study presents a sign language recognition system that is capable of recognizing Arabic Sign Language from recorded RGB videos. To achieve this, two datasets were considered, such as (1) the raw dataset and (2) the face–hand region-based segmented dataset produced from the raw dataset. Moreover, operational layer-based multi-layer perceptron “SelfMLP” is proposed in this study to build CNN-LSTM-SelfMLP models for Arabic Sign Language recognition. MobileNetV2 and ResNet18-based CNN backbones and three SelfMLPs were used to construct six different models of CNN-LSTM-SelfMLP architecture for performance comparison of Arabic Sign Language recognition. This study examined the signer-independent mode to deal with real-time application circumstances. As a result, MobileNetV2-LSTM-SelfMLP on the segmented dataset achieved the best accuracy of 87.69% with 88.57% precision, 87.69% recall, 87.72% F1 score, and 99.75% specificity. Overall, face–hand region-based segmentation and SelfMLP-infused MobileNetV2-LSTM-SelfMLP surpassed the previous findings on Arabic Sign Language recognition by 10.970% accuracy.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s23167156" target="_blank">https://dx.doi.org/10.3390/s23167156</a></p>2023-08-14T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s23167156https://figshare.com/articles/journal_contribution/Signer-Independent_Arabic_Sign_Language_Recognition_System_Using_Deep_Learning_Model/26798149CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/267981492023-08-14T09:00:00Z |
| spellingShingle | Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model Kanchon Kanti Podder (19459564) Information and computing sciences Artificial intelligence Computer vision and multimedia computation Human-centred computing Arabic Sign Language deep learning dynamic sign language segmentation MediaPipe |
| status_str | publishedVersion |
| title | Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model |
| title_full | Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model |
| title_fullStr | Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model |
| title_full_unstemmed | Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model |
| title_short | Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model |
| title_sort | Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model |
| topic | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Human-centred computing Arabic Sign Language deep learning dynamic sign language segmentation MediaPipe |