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Title: :  Sign Language Recognition using Deep Learning: A Review of Methods and Future Directions
PaperId: :  25452
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 6 2024
DUI:    16.0415/IJARIIE-25452
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Shashank Singh ShekhawatPoornima Institute of Engineering and Technology
Anurag Anand DuveyPoornima Institute of Engineering and Technology

Abstract

Computer Engineering
Deep Learning, Hand-Sign Recognition, LSTM, CNN, MediaPipe, Sign-Recognitions Methods
Sign language plays a vital role in facilitating communication for individuals who are hearing-impaired or have speech difficulties. The increasing integration of artificial intelligence and neural networks has greatly accelerated the progress of sign language recognition systems, effectively closing the communication gap between hearing and non-hearing individuals. This paper reviews three key approaches leveraging neural networks: static gesture recognition using convolutional neural networks (cnns), dynamic sequence modeling with long short-term memory (lstm) networks, and real-time gesture processing through mediapipe-integrated architectures. While convolutional neural networks excel at recognizing static gestures, long short-term memory networks demonstrate superior capabilities in handling sequential and dynamic gestures. Each approach is assessed based on its methodology, the data used, its accuracy, and how well it can be applied in real-life situations. The review points out important areas that require further investigation, such as the variety of datasets, the ability to withstand environmental changes, and the efficiency of computational processes, emphasizing the importance of combining different types of data and developing scalable systems. Looking ahead, the focus is on incorporating transformers, lightweight models for edge devices, and seamless integration into wearable and virtual platforms. By overcoming current constraints and embracing cutting-edge technologies, this field has the potential to significantly improve accessibility and promote inclusivity for individuals with hearing impairments.

Citations

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IJARIIE Shashank Singh Shekhawat, and Anurag Anand Duvey. "Sign Language Recognition using Deep Learning: A Review of Methods and Future Directions" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 6 2024 Page 1438-1442
MLA Shashank Singh Shekhawat, and Anurag Anand Duvey. "Sign Language Recognition using Deep Learning: A Review of Methods and Future Directions." International Journal Of Advance Research And Innovative Ideas In Education 10.6(2024) : 1438-1442.
APA Shashank Singh Shekhawat, & Anurag Anand Duvey. (2024). Sign Language Recognition using Deep Learning: A Review of Methods and Future Directions. International Journal Of Advance Research And Innovative Ideas In Education, 10(6), 1438-1442.
Chicago Shashank Singh Shekhawat, and Anurag Anand Duvey. "Sign Language Recognition using Deep Learning: A Review of Methods and Future Directions." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 6 (2024) : 1438-1442.
Oxford Shashank Singh Shekhawat, and Anurag Anand Duvey. 'Sign Language Recognition using Deep Learning: A Review of Methods and Future Directions', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 6, 2024, p. 1438-1442. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Sign_Language_Recognition_using_Deep_Learning__A_Review_of_Methods_and_Future_Directions_ijariie25452.pdf (Accessed : ).
Harvard Shashank Singh Shekhawat, and Anurag Anand Duvey. (2024) 'Sign Language Recognition using Deep Learning: A Review of Methods and Future Directions', International Journal Of Advance Research And Innovative Ideas In Education, 10(6), pp. 1438-1442IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Sign_Language_Recognition_using_Deep_Learning__A_Review_of_Methods_and_Future_Directions_ijariie25452.pdf (Accessed : )
IEEE Shashank Singh Shekhawat, and Anurag Anand Duvey, "Sign Language Recognition using Deep Learning: A Review of Methods and Future Directions," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 6, pp. 1438-1442, Nov-Dec 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Sign_Language_Recognition_using_Deep_Learning__A_Review_of_Methods_and_Future_Directions_ijariie25452.pdf [Accessed : ].
Turabian Shashank Singh Shekhawat, and Anurag Anand Duvey. "Sign Language Recognition using Deep Learning: A Review of Methods and Future Directions." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 6 ().
Vancouver Shashank Singh Shekhawat, and Anurag Anand Duvey. Sign Language Recognition using Deep Learning: A Review of Methods and Future Directions. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(6) : 1438-1442. Available from: https://ijariie.com/AdminUploadPdf/Sign_Language_Recognition_using_Deep_Learning__A_Review_of_Methods_and_Future_Directions_ijariie25452.pdf
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