Open Access Open Access  Restricted Access Subscription Access

Recent Trends in Sign Language Recognition

Seema Sabharwal, Priti Singla

Abstract


Artificial Intelligence technologies have the potential to play a large part in removing the communication hurdles that persons who are deaf or hearing-impaired have when interacting with other groups, therefore making a substantial contribution to the social inclusion of these individuals. Recent developments in sensing technologies and artificial intelligence algorithms have opened the way for the creation of a variety of applications to meet the requirements of communities of people who are deaf or hearing impaired. For this reason, the purpose of this study is to give a complete evaluation of state-of-the-art approaches in sign language recognition, translation, and representation, highlighting the benefits and drawbacks of each technique. In addition, the study shows a variety of applications while simultaneously discussing the primary obstacles that are present in the sector of sign language technology. In addition, a future research path is suggested to assist potential researchers who are interested in further improving the topic.


Full Text:

PDF

References


S. Dalal, R. Kacheria, and V. Venkataramanan, “A Comparative Study on Sign Language Recognition Methods,” presented at the Proceedings of the 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022, 2022. doi: 10.1109/ICSES55317.2022.9914329.

I. A. Adeyanju, O. O. Bello, and M. A. Adegboye, “Machine learning methods for sign language recognition: A critical review and analysis,” Intelligent Systems with Applications, vol. 12, p. 200056, Nov. 2021, doi: 10.1016/j.iswa.2021.200056.

T. Aadit, J. Deepak, P. Janhavi, and D. Jyoti, “Video chat application for mutes,” presented at the 2021 International Conference on Emerging Smart Computing and Informatics, ESCI 2021, 2021, pp. 181–185. doi: 10.1109/ESCI50559.2021.9397044.

Seema and P. Singla, “A Comprehensive Review of CNN-Based Sign Language Translation System,” in Proceedings of Data Analytics and Management, A. Khanna, Z. Polkowski, and O. Castillo, Eds., in Lecture Notes in Networks and Systems, vol. 572. Singapore: Springer Nature Singapore, 2023, pp. 347–362. doi: 10.1007/978-981-19-7615-5_31.

M. S. Bajwa and G. C. Gandhi, “A Systematic Review of Machine Learning Approaches for Classifying Indian Sign Language Gestures and Facial Expressions,” presented at the 4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022 - Proceedings, 2022, pp. 848–853. doi: 10.1109/ICIRCA54612.2022.9985747.

G. Anantha Rao, P. V. V. Kishore, A. S. C. S. Sastry, D. Anil Kumar, and E. Kiran Kumar, “Selfie continuous sign language recognition with neural network classifier,” presented at the Lecture Notes in Electrical Engineering, 2018, pp. 31–40. doi: 10.1007/978-981-10-4280-5_4.

D. D. Chakladar, P. Kumar, S. Mandal, P. P. Roy, M. Iwamura, and B.-G. Kim, “3d avatar approach for continuous sign movement using speech/text,” Applied Sciences (Switzerland), vol. 11, no. 8, 2021, doi: 10.3390/app11083439.

Y. Liao, P. Xiong, W. Min, W. Min, and J. Lu, “Dynamic Sign Language Recognition Based on Video Sequence With BLSTM-3D Residual Networks,” IEEE Access, vol. 7, pp. 38044–38054, 2019, doi: 10.1109/ACCESS.2019.2904749.

S. Aly and W. Aly, “DeepArSLR: A Novel Signer-Independent Deep Learning Framework for Isolated Arabic Sign Language Gestures Recognition,” IEEE Access, vol. 8, pp. 83199–83212, 2020, doi: 10.1109/ACCESS.2020.2990699.

Ç. Gökçe, O. Özdemir, A. A. Kındıroğlu, and L. Akarun, “Score-Level Multi Cue Fusion for Sign Language Recognition,” in Computer Vision – ECCV 2020 Workshops, A. Bartoli and A. Fusiello, Eds., in Lecture Notes in Computer Science, vol. 12536. Cham: Springer International Publishing, 2020, pp. 294–309. doi: 10.1007/978-3-030-66096-3_21.


Refbacks

  • There are currently no refbacks.