Translation System for Sign Language Learning
DOI:
https://doi.org/10.32628/CSEIT2410257Keywords:
Natural Language Processing , Sign Language LearningAbstract
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.
Downloads
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).
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
