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Research Article
Instant Sign Language Translation to Text and Speech via CNN
V.Karpagam1
Hariharan Balachandran2
K.R Aakash3
R.G. Kabilesh Kannan4
1Assistant Professor Department of Computer Science and Engineering, K.L.N. College of Engineering, Sivagangai, Tamilnadu, India. 234 Final Year Students, Department of Computer Science and Engineering, K.L.N. College of Engineering, Sivagangai, Tamilnadu, India.
Published Online: September-October 2024
Pages: 40-42
Cite this article
No DOIReferences
1. Joao Carreira andA. Zisserman “ An efficient two stream network for isolated sign language Recognition” IEEE Xplore
Vol.no:10,Published 2022
2. A. A. Barbhuiya, R. K. Karsh, and R. Jain, ‘‘CNN based feature extraction 918 and classification for sign language,’’ Multimedia
Tools Appl., vol. 80, 919 no. 2, pp. 3051–3069, 2021.
3. A. A. I. Sidig, H. Luqman, S. Mahmoud, and M. Mohandes, ‘‘KArSL: 968 Arabic sign language database,’’ ACM Trans. Asian LowResource Lang. 969 Inf. Process., vol. 20, no. 1, pp. 1–19, Jan. 2021.
4. K. B. S. Rao, Hermann Ney, Anil K. Jain, M. L. G. Joy computer science and education on IEEE Access, Vol.no:8, ppl67341, 2023
5. H. Luqman and E.-S.-M. El-Alfy, ‘‘Towards hybrid multimodal manual and non-manual Arabic sign language recognition: MArSL
database and pilot study,’’ Electronics, vol. 10, no. 14, p. 1739, Jul. 2021.
6. A. A. I. Sidig, H. Luqman, S. Mahmoud, and M. Mohandes, ‘‘KArSL: Arabic sign language database,’’ ACM Trans. Asian LowResource Lang. Inf. Process., vol. 20, no. 1, pp. 1–19, Jan. 2021.
7. J. Imran and B. Raman, ‘‘Deep motion templates and extreme learning machine for sign language recognition,’’ Vis. Comput., vol.
36, no. 6, pp. 1233–1246, Jun. 2020.
8. Chen, L., & Wang, Y. (2023). "Enhancing Accessibility: A Sign Language Recognition System with Real-Time TTS Output." Journal
of Accessibility and Design for All, 13(2), 102-118.
9. .Zhang, H., & Zhang, W. (2022). "Real-time Sign Language Recognition Using CNNs and MediaPipe." Journal of Machine Learning
Research, 23(45), 1-20.
10. B.; Zdravevski, E.; Lameski, P.; Pires, I.M.; Melero, F.J.; Martinez, T.P.; Garcia, N.M.; Mihajlov, M.; Chorbev, I.; Trajkovik, V.
Technological Solutions for Sign Language Recognition: A Scoping Review of Research Trends, Challenges, and Opportunities. IEEE
Access 2022, 10, 40979–40998.
Vol.no:10,Published 2022
2. A. A. Barbhuiya, R. K. Karsh, and R. Jain, ‘‘CNN based feature extraction 918 and classification for sign language,’’ Multimedia
Tools Appl., vol. 80, 919 no. 2, pp. 3051–3069, 2021.
3. A. A. I. Sidig, H. Luqman, S. Mahmoud, and M. Mohandes, ‘‘KArSL: 968 Arabic sign language database,’’ ACM Trans. Asian LowResource Lang. 969 Inf. Process., vol. 20, no. 1, pp. 1–19, Jan. 2021.
4. K. B. S. Rao, Hermann Ney, Anil K. Jain, M. L. G. Joy computer science and education on IEEE Access, Vol.no:8, ppl67341, 2023
5. H. Luqman and E.-S.-M. El-Alfy, ‘‘Towards hybrid multimodal manual and non-manual Arabic sign language recognition: MArSL
database and pilot study,’’ Electronics, vol. 10, no. 14, p. 1739, Jul. 2021.
6. A. A. I. Sidig, H. Luqman, S. Mahmoud, and M. Mohandes, ‘‘KArSL: Arabic sign language database,’’ ACM Trans. Asian LowResource Lang. Inf. Process., vol. 20, no. 1, pp. 1–19, Jan. 2021.
7. J. Imran and B. Raman, ‘‘Deep motion templates and extreme learning machine for sign language recognition,’’ Vis. Comput., vol.
36, no. 6, pp. 1233–1246, Jun. 2020.
8. Chen, L., & Wang, Y. (2023). "Enhancing Accessibility: A Sign Language Recognition System with Real-Time TTS Output." Journal
of Accessibility and Design for All, 13(2), 102-118.
9. .Zhang, H., & Zhang, W. (2022). "Real-time Sign Language Recognition Using CNNs and MediaPipe." Journal of Machine Learning
Research, 23(45), 1-20.
10. B.; Zdravevski, E.; Lameski, P.; Pires, I.M.; Melero, F.J.; Martinez, T.P.; Garcia, N.M.; Mihajlov, M.; Chorbev, I.; Trajkovik, V.
Technological Solutions for Sign Language Recognition: A Scoping Review of Research Trends, Challenges, and Opportunities. IEEE
Access 2022, 10, 40979–40998.
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