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Research Article
Kenyan Sign Language Recognition Using Ensemble Method
Stanley Rotich1
David Muriuki2
Andrew Kipkebut3
1Department of Mathematics and Computing, Cooperative University of Kenya, Nairobi, Kenya. 2Department of Mathematics and Statistic Machakos University, Nairobi, Kenya. 3Department of Computer Science & Information Technology Kabarak University, Nakuru, Kenya.
Published Online: September-December 2025
Pages: 127-133
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403023
References
1. Ang J. et. al (2023) Dataglove for Sign Language Recognition of People with Hearing and Speech Impairment via Wearable Inertial Sensors, https://doi.org/10.3390/s23156693 https://www.mdpi.com/journal/sensors
2. Ankita, W. • Parteek, K. (2020) Deep learning-based sign language recognition system for static signs, Neural Computing and Applications (2020) 32:7957–7968 https://doi.org/10.1007/s00521-019-04691-y
3. Bantupalli, K., & Xie, Y. (2019). American Sign Language Recognition using Deep Learning and Computer Vision. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 4896–4899. https://doi.org/10.1109/BigData.2018.8622141
4. Crowe, K., Marschark, M., Dammeyer, J., & Lehane, C. (2017). Achievement, language, and technology use among college-bound deaf learners. Journal of Deaf Studies and Deaf Education, 22(4), 393–401. https://doi.org/10.1093/deafed/enx029
5. Dabre, K. & Dholay, S. (2014). Machine learning model for sign language interpretation using webcam images. International conference on circuit, Systems, Communication and Information Technology and Application (CSCITA). PP317-321 https://doi.org/10.1109/CSCITA.2014.6839279
6. Himanshu G., Aniruddh R., Jasmin T. (2018) Vision Based Approach to Sign Language Recognition, International Journal of Advances in Applied Sciences (IJAAS) Vol. 7, No. 2, June 2018, pp. 156~161 ISSN: 2252-8814, https://doi.org/10.11591/ijaas.v7.i2.pp156-161
7. Himanshu, G., Aniruddh, R., Jasmin. (2018). Vision-Based Approach to Sign Language Recognition. School of Computer Engineering, VIT University T, Vellore-632014, Indi
8. Lucas, C., & Bayley, R. (2011). Variation in sign languages: Recent research on ASL and beyond. Linguistics and Language Compass, 5(9), 677–690. https://doi.org/10.1111/j.1749-818X.2011.00304.x
9. panelYuxuan L. et al. wearable system for sign language recognition enabled by a convolutional neural network Nano Energy Volume 116, nternational Journal of Advances in Applied Sciences (IJAAS) Vol. 7, No. 2, June 2018, pp. 156~161 ISSN: 2252-8814, https://doi.org/10.11591/ijaas.v7.i2.pp156-161
10. Patel, K. (2022). An Assistance System for Deaf, Dumb, Blind, and Learning Disability Individuals. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 06(05). https://doi.org/10.55041/ijsrem13314
11. Samarth K. & Kabir N. (2023), Sign Language Interpretation using Ensembled Deep Learning Models, ITM Web of Conferences 53, 01003 https://doi.org/10.1051/itmconf/20235301003
12. Subburaj, S., Murugavalli S., (2022) Survey on sign language recognition in context of vision-based and deep learning https://doi.org/10.1016/j.measen.2022.100385
13. Vijeeta, P. et al. (2022) A Deep Learning Framework for Real-Time Sign Language Recognition Based on Transfer Learning, International Journal of Engineering Trends and Technology Volume 70 Issue 6, 32-41, June 2022 ISSN: 2231 – 5381 / https://doi.org/10.14445/22315381/IJETT-V70I6P204
14. Wanjala, G. K. (2023). A Model for sign language recognition for Kenyan Sign-Language [Strathmore University]. http://hdl.handle.net/11071/13530
15. Zhihao Z. et al. (2020) Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays Nature Electronics volume 3, pages571–578 Cite this article
2. Ankita, W. • Parteek, K. (2020) Deep learning-based sign language recognition system for static signs, Neural Computing and Applications (2020) 32:7957–7968 https://doi.org/10.1007/s00521-019-04691-y
3. Bantupalli, K., & Xie, Y. (2019). American Sign Language Recognition using Deep Learning and Computer Vision. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 4896–4899. https://doi.org/10.1109/BigData.2018.8622141
4. Crowe, K., Marschark, M., Dammeyer, J., & Lehane, C. (2017). Achievement, language, and technology use among college-bound deaf learners. Journal of Deaf Studies and Deaf Education, 22(4), 393–401. https://doi.org/10.1093/deafed/enx029
5. Dabre, K. & Dholay, S. (2014). Machine learning model for sign language interpretation using webcam images. International conference on circuit, Systems, Communication and Information Technology and Application (CSCITA). PP317-321 https://doi.org/10.1109/CSCITA.2014.6839279
6. Himanshu G., Aniruddh R., Jasmin T. (2018) Vision Based Approach to Sign Language Recognition, International Journal of Advances in Applied Sciences (IJAAS) Vol. 7, No. 2, June 2018, pp. 156~161 ISSN: 2252-8814, https://doi.org/10.11591/ijaas.v7.i2.pp156-161
7. Himanshu, G., Aniruddh, R., Jasmin. (2018). Vision-Based Approach to Sign Language Recognition. School of Computer Engineering, VIT University T, Vellore-632014, Indi
8. Lucas, C., & Bayley, R. (2011). Variation in sign languages: Recent research on ASL and beyond. Linguistics and Language Compass, 5(9), 677–690. https://doi.org/10.1111/j.1749-818X.2011.00304.x
9. panelYuxuan L. et al. wearable system for sign language recognition enabled by a convolutional neural network Nano Energy Volume 116, nternational Journal of Advances in Applied Sciences (IJAAS) Vol. 7, No. 2, June 2018, pp. 156~161 ISSN: 2252-8814, https://doi.org/10.11591/ijaas.v7.i2.pp156-161
10. Patel, K. (2022). An Assistance System for Deaf, Dumb, Blind, and Learning Disability Individuals. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 06(05). https://doi.org/10.55041/ijsrem13314
11. Samarth K. & Kabir N. (2023), Sign Language Interpretation using Ensembled Deep Learning Models, ITM Web of Conferences 53, 01003 https://doi.org/10.1051/itmconf/20235301003
12. Subburaj, S., Murugavalli S., (2022) Survey on sign language recognition in context of vision-based and deep learning https://doi.org/10.1016/j.measen.2022.100385
13. Vijeeta, P. et al. (2022) A Deep Learning Framework for Real-Time Sign Language Recognition Based on Transfer Learning, International Journal of Engineering Trends and Technology Volume 70 Issue 6, 32-41, June 2022 ISSN: 2231 – 5381 / https://doi.org/10.14445/22315381/IJETT-V70I6P204
14. Wanjala, G. K. (2023). A Model for sign language recognition for Kenyan Sign-Language [Strathmore University]. http://hdl.handle.net/11071/13530
15. Zhihao Z. et al. (2020) Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays Nature Electronics volume 3, pages571–578 Cite this article
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