Translation System for Sign Language Learning

Authors

  • Jibin Joy Assistant Professor, Department of Computer Science, Yuvakshetra Institute of Management Studies, Palakkad, Kerala, India Author
  • N Meenakshi Department of Computer Science, Yuvakshetra Institute of Management Studies, Palakkad, Kerala, India Author
  • Thejas Vinodh, Department of Computer Science, Yuvakshetra Institute of Management Studies, Palakkad, Kerala, India Author
  • Abel Thomas Department of Computer Science, Yuvakshetra Institute of Management Studies, Palakkad, Kerala, India Author
  • Shifil S Department of Computer Science, Yuvakshetra Institute of Management Studies, Palakkad, Kerala, India Author

DOI:

https://doi.org/10.32628/CSEIT2410257

Keywords:

Natural Language Processing , Sign Language Learning

Abstract

Sign language display software converts text/speech to animated sign language to support the special needs population, aiming to enhance communication comfort, health, and productivity. Advancements in technology, particularly computer systems, enable the development of innovative solutions to address the unique needs of individuals with special requirements, potentially enhancing their mental well-being. Using Python and NLP, a process has been devised to detect text and live speech, converting it into animated sign language in real-time. Blender is utilized for animation and video processing, while datasets and NLP are employed to train and convert text to animation. This project aims to cater to a diverse range of users across different countries where various sign languages are prevalent. By bridging the gap between linguistic and cultural differences, such software not only facilitates communication but also serves as an educational tool. Overall, it offers a cost-effective and widely applicable solution to promote inclusivity and accessibility.

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References

T. Starner, J. Weaver, A. Pentland. Real-Time American Sign Language Recognition from Video Using Hidden Markov Models, M.I.T. Media Laboratory Perceptual Computing Section Technical Report No. 375, 1996. DOI: https://doi.org/10.1007/978-94-015-8935-2_10

H. Hienz, K. Grobel, Automatic Estimation of Body Regions from Video Images, in I. Wachsmuth and M. Fröhlich (Eds.) Gesture and Sign Language in Human Computer Interaction (Berlin: Springer-Verlag,1998), pp. 135-145. DOI: https://doi.org/10.1007/BFb0052995

S. He. (2019). Research of a Sign Language Translation System Based on Deep Learning, pp. 392- 396. 10.1109/AIAM48774.2019.00083. DOI: https://doi.org/10.1109/AIAM48774.2019.00083

Herath, H.C.M. & W.A.L.V. Kumari, &Senevirathne, W.A.P.B &Dissanayake, Maheshi. (2013). IMAGE BASED SIGN LANGUAGE RECOGNITION SYSTEM FOR SINHALA SIGN LANGUAGE.

M. Geetha and U. C. Manjusha, ―A Vision Based Recognition of Indian Sign

Language Alphabets and Numerals Using B-Spline Approximation‖, International Journal on Computer Science and Engineering (IJCSE), vol. 4, no. 3, pp. 406-415. 2012.

Pigou L., Dieleman S., Kindermans PJ., Schrauwen B. (2015) Sign Language Recognition Using Convolutional Neural Networks. In: Agapito L., Bronstein M., Rother C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science, vol. 8925. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-16178-5_40

Escalera S., Baró X., Gonzàlez J., Bautista M.A., Madadi, M., Reyes M., PonceLópez V, Escalante H.J., Shotton J., Guyon, I. (2014). ChaLearn Looking at People Challenge

: Dataset and Results. Workshop at the European Conference on Computer Vision (pp. 459473). Springer, Cham.

Huang J., Zhou W., & Li H. (2015). Sign Language Recognition using 3D convolutional neural networks. IEEE International Conference on Multimedia and Expo (ICME) (pp. 16). Turin: IEEE.

Edon Mustafa. Sign Language Interpretation using Kinect, MSc in Software Engineering and Telecommunications, The University of Sheffield. 2014.

https://www.kaggle.com/ - Sign Language MNIST Dataset.(Accessed on 25th July, 2022)

R. Rumana, ReddygariSandhya Rani, and R. Prema, ―A Review Paper on Sign Language Recognition for The Deaf and Dumb‖. (2021).

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Published

13-04-2024

Issue

Section

Research Articles

How to Cite

[1]
Jibin Joy, N Meenakshi, Thejas Vinodh, Abel Thomas, and Shifil S, “Translation System for Sign Language Learning”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 2, pp. 487–493, Apr. 2024, doi: 10.32628/CSEIT2410257.

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