A Comprehensive Study on Sign Language Recognition for Deaf and Dumb people
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Keywords

Sign Language Recognition (SLR)
Convolution Neural Network (CNN)
Support Vector Machine (SVM)
Indian Sign Language (ISL)
FFT

How to Cite

Vaidhya, G. K., and C. A. S. Deiva Preetha. 2022. “A Comprehensive Study on Sign Language Recognition for Deaf and Dumb People”. Journal of Trends in Computer Science and Smart Technology 4 (3): 163-74. https://doi.org/10.36548/jtcsst.2022.3.005.

Abstract

There are roughly 72 million ‘hard of hearing’ individuals all over the planet, and more than 80% of them live in developing countries, as indicated in a review by the World Federation for the Deaf. Their lives are hindered by hearing distortions which bar them from showing full interest in the public besides taking pleasure in enjoying identical privileges. Motion based communication is common for the people with hearing and speaking impairments. Communication through signs is a successful choice rather than talking, where the former is replaced by hand flags. One solution to this problem is to study text comprehension tasks for hearing impaired localities using Sign Language Recognition. Gesture-based communication is the most significant and centered approach of communication for deaf and dumb individuals. This paper gives a concise review of different examination works conducted thus far in this field.

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References

V. Gupta, M. Jain and G. Aggarwal, "Sign Language to Text for Deaf and Dumb," 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2022, pp. 384-389, doi: 10.1109/Confluence52989.2022.9734196.

B. Ben Atitallah et al., "Hand Sign Recognition System Based on EIT Imaging and Robust CNN Classification," in IEEE Sensors Journal, vol. 22, no. 2, pp. 1729-1737, 15 Jan.15, 2022, doi: 10.1109/JSEN.2021.3130982

Q. M. Areeb, Maryam, M. Nadeem, R. Alroobaea and F. Anwer, "Helping Hearing- Impaired in Emergency Situations: A Deep Learning-Based Approach," in IEEE Access, vol. 10, pp. 8502-8517, 2022, doi: 10.1109/ACCESS.2022.3142918.

Y. Zhou, C. Ji and L. Cao, "Research on Optimizer Algorithm of Sign Language Recognition Model," 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), 2022, pp. 102-106, doi: 10.1109/ICPECA53709.2022.9719010.

H. Zhou, W. Zhou, Y. Zhou and H. Li, "Spatial- Temporal Multi-Cue Network for Sign Language Recognition and Translation," in IEEE Transactions on Multimedia, vol. 24, pp. 768- 779, 2022, doi: 10.1109/TMM.2021.3059098

Z. Hein, T. P. Htoo, B. Aye, S. M. Htet and K. Z. Ye, "Leap Motion based Myanmar Sign Language Recognition using Machine Learning," 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), 2021, pp. 2304-2310, doi: 10.1109/ElConRus51938.2021.9396496.

B. Sonare, A. Padgal, Y. Gaikwad and A. Patil, "Video-Based Sign Language Translation System Using Machine Learning," 2021 2nd International Conference for Emerging Technology (INCET), 2021, pp. 1-4, doi: 10.1109/INCET51464.2021.9456176.

I. H. Yemenoglu, A. F. M. S. Shah and H. Ilhan, "Deep Convolutional Neural Networks- Based Sign Language Recognition System," 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2021, pp. 0573-0576, doi: 10.1109/IEMCON53756.2021.9623068.

M. Bansal and S. Gupta, "Detection and Recognition of Hand Gestures for Indian Sign Language Recognition System," 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), 2021, pp. 136- 140, doi: 10.1109/ISPCC53510.2021.9609448.

M. Varsha and C. S. Nair, "Indian Sign Language Gesture Recognition Using Deep Convolutional Neural Network," 2021 8th International Conference on Smart Computing and Communications (ICSCC), 2021, pp. 193- 197, doi: 10.1109/ICSCC51209.2021.9528246.

B. Joksimoski et al., "Technological solutions for sign language recognition: a scoping review of research trends, challenges, and opportunities," in IEEE Access, doi: 10.1109/ACCESS.2022.3161440.

X. Han, F. Lu, J. Yin, G. Tian and J. Liu, "Sign Language Recognition Based on R(2+1)D With Spatial–Temporal–Channel Attention," in IEEE Transactions on Human-Machine Systems, doi: 10.1109/THMS.2022.3144000.

A. Alqahtani et al., "Improving the Virtual Educational Platforms for the Deaf and Dumb under the Covid-19 Pandemic Circumstances," 2022 2nd International Conference on Computing and Information Technology (ICCIT), 2022, pp. 191-196, doi: 10.1109/ICCIT52419.2022.9711613.

Y. C. Bilge, R. G. Cinbis and N. Ikizler-Cinbis, "Towards Zero-shot Sign Language Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2022.3143074.

O. MercanogluSincan and H. Y. Keles, "Using Motion History Images With 3D Convolutional Networks in Isolated Sign Language Recognition," in IEEE Access, vol. 10, pp. 18608-18618, 2022, doi: 10.1109/ACCESS.2022.3151362.

M. Al-Qurishi, T. Khalid and R. Souissi, "Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues," in IEEE Access, vol. 9, pp. 126917- 126951, 2021, doi: 10.1109/ACCESS.2021.3110912.

R. Singh and M. Jangid, "Indian Sign language Recognition Using Color Space Model and Thresholding," 2021 Asian Conference on Innovation in Technology (ASIANCON), 2021, pp. 1-4, doi: 10.1109/ASIANCON51346.2021.9544615.

C. Chu, Q. Xiao, J. Xiao and C. Gao, "Sign Language Action Recognition System Based on Deep Learning," 2021 5th International Conference on Automation, Control and Robots (ICACR), 2021, pp. 24-28, doi: 10.1109/ICACR53472.2021.9605168.

W. Li, H. Pu and R. Wang, "Sign Language Recognition Based on Computer Vision," 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), 2021, pp. 919-922, doi: 10.1109/ICAICA52286.2021.9498024.

S. Tornay, M. Razavi and M. Magimai.-Doss, "Towards Multilingual Sign Language Recognition," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 6309-6313, doi: 10.1109/ICASSP40776.2020.9054631.

Y. Zhang and L. Cao, "A Survey on Neural Machine Translation Applied to Sign Language Generation," 2021 3rd International Conference on Applied Machine Learning (ICAML), 2021, pp. 413-417, doi:10.1109/ICAML54311.2021.00093.

H. N. Saha, S. Tapadar, S. Ray, S. K. Chatterjee and S. Saha, "A Machine Learning Based Approach for Hand Gesture Recognition using Distinctive Feature Extraction," 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), 2018, pp. 91-98, doi: 10.1109/CCWC.2018.8301631.

Suharjito, H. Gunawan, N. Thiracitta and A. Nugroho, "Sign Language Recognition Using Modified Convolutional Neural Network Model," 2018 Indonesian Association for Pattern Recognition International Conference (INAPR), 2018, pp. 1-5, doi: 10.1109/INAPR.2018.8627014.

M. Xie and X. Ma, "End-to-End Residual Neural Network with Data Augmentation for Sign Language Recognition," 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2019, pp. 1629-1633, doi: 10.1109/IAEAC47372.2019.8998073.

B. Gupta, P. Shukla and A. Mittal, "K-nearest correlated neighbor classification for Indian sign language gesture recognition using feature fusion," 2016 International Conference on Computer Communication and Informatics (ICCCI), 2016, pp. 1-5, doi: 10.1109/ICCCI.2016.7479951.