Two stream GRU model with ELU activation function for sign language recognition
Pose Estimation features have been successfully used in human activity recognition including sign language recognition. One of the key challenges in sign language recognition is handling signer-independent modes and hand dominance of signer. This paper proposes the use of the Gated Recurrent Unit (G...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305325000390 |
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| author | Kasian Myagila Devotha Godfrey Nyambo Mussa Ally Dida |
| author_facet | Kasian Myagila Devotha Godfrey Nyambo Mussa Ally Dida |
| author_sort | Kasian Myagila |
| collection | DOAJ |
| description | Pose Estimation features have been successfully used in human activity recognition including sign language recognition. One of the key challenges in sign language recognition is handling signer-independent modes and hand dominance of signer. This paper proposes the use of the Gated Recurrent Unit (GRU) with the ELU activation function to improve computation efficiency and to enhance model learning efficiency. In addition, the paper proposes two stream model architecture to address the challenge of left and right-hand dominance. The study developed model using a Tanzania Sign language datasets collected using mobile devices and extracted pose estimation feature using MediaPipe holistic framework. According to the results, the proposed model not only achieves an impressive overall accuracy of 95%, but also trains more efficiently than comparable algorithms. Particularly in the signer-independent mode, the two-stream approach led to substantial improvements, achieving a maximum accuracy of 92% and a minimum accuracy of 70% with significant increase on the left handed signer accuracy by 37%. The results highlight the effectiveness of the two-stream approach in overcoming challenges related to left and right-hand dominance, which often arise from signer-specific hand dominance. Additionally, the results indicate that, the proposed model can have a positive impact on limited computational resources while also enhancing the model’s overall performance. |
| format | Article |
| id | doaj-art-abaa178594cf44a7bbae718977788036 |
| institution | DOAJ |
| issn | 2667-3053 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-abaa178594cf44a7bbae7189777880362025-08-20T03:09:27ZengElsevierIntelligent Systems with Applications2667-30532025-06-012620051310.1016/j.iswa.2025.200513Two stream GRU model with ELU activation function for sign language recognitionKasian Myagila0Devotha Godfrey Nyambo1Mussa Ally Dida2School of Computation and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania; Department of Computing Science Studies, Mzumbe University, P.O. Box 87, Morogoro, Tanzania; Corresponding author at: Department of Computing Science Studies, Mzumbe University, P.O. Box 87, Morogoro, Tanzania.School of Computation and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, TanzaniaSchool of Computation and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, TanzaniaPose Estimation features have been successfully used in human activity recognition including sign language recognition. One of the key challenges in sign language recognition is handling signer-independent modes and hand dominance of signer. This paper proposes the use of the Gated Recurrent Unit (GRU) with the ELU activation function to improve computation efficiency and to enhance model learning efficiency. In addition, the paper proposes two stream model architecture to address the challenge of left and right-hand dominance. The study developed model using a Tanzania Sign language datasets collected using mobile devices and extracted pose estimation feature using MediaPipe holistic framework. According to the results, the proposed model not only achieves an impressive overall accuracy of 95%, but also trains more efficiently than comparable algorithms. Particularly in the signer-independent mode, the two-stream approach led to substantial improvements, achieving a maximum accuracy of 92% and a minimum accuracy of 70% with significant increase on the left handed signer accuracy by 37%. The results highlight the effectiveness of the two-stream approach in overcoming challenges related to left and right-hand dominance, which often arise from signer-specific hand dominance. Additionally, the results indicate that, the proposed model can have a positive impact on limited computational resources while also enhancing the model’s overall performance.http://www.sciencedirect.com/science/article/pii/S2667305325000390Sign language recognitionGRUELU functionPose estimationSigner independent |
| spellingShingle | Kasian Myagila Devotha Godfrey Nyambo Mussa Ally Dida Two stream GRU model with ELU activation function for sign language recognition Intelligent Systems with Applications Sign language recognition GRU ELU function Pose estimation Signer independent |
| title | Two stream GRU model with ELU activation function for sign language recognition |
| title_full | Two stream GRU model with ELU activation function for sign language recognition |
| title_fullStr | Two stream GRU model with ELU activation function for sign language recognition |
| title_full_unstemmed | Two stream GRU model with ELU activation function for sign language recognition |
| title_short | Two stream GRU model with ELU activation function for sign language recognition |
| title_sort | two stream gru model with elu activation function for sign language recognition |
| topic | Sign language recognition GRU ELU function Pose estimation Signer independent |
| url | http://www.sciencedirect.com/science/article/pii/S2667305325000390 |
| work_keys_str_mv | AT kasianmyagila twostreamgrumodelwitheluactivationfunctionforsignlanguagerecognition AT devothagodfreynyambo twostreamgrumodelwitheluactivationfunctionforsignlanguagerecognition AT mussaallydida twostreamgrumodelwitheluactivationfunctionforsignlanguagerecognition |