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Title: :  Sign Language Generation and Detection using Deep Learning
PaperId: :  25805
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 1 2025
DUI:    16.0415/IJARIIE-25805
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Ritesh Kiran DeoreDr. Vithalrao Vikhe Patil College of Engineering
Shivam Dadasaheb GavhaneDr. Vithalrao Vikhe Patil College of Engineering
Naeem Rajjak SayyadDr. Vithalrao Vikhe Patil College of Engineering
Sanket Rajendra ZendeDr. Vithalrao Vikhe Patil College of Engineering
Vidya Vinod JagtapDr. Vithalrao Vikhe Patil College of Engineering

Abstract

Deep Learning
Sign Language Generation and Detection, Deep Learning, CNN, Natural Language Processing, Computer Vision
This study focuses on developing innovative two-way communication systems that allow for seamless interactions between people who use sign language and those who do not. The system uses deep learning techniques, particularly annoying neural networks (CNNs), to enable real-time translation between text, audio and sign language. The main purpose of this project is to bridge communication gaps and to provide access, more efficient and integrated daily interactions for the deaf and hearing at community hearings. In sign language, the difficulty is communicating with people who are not used to it. While existing solutions exist, such as sign language interpreters and mobile applications, they are often unrealistic, expensive or unavailable in real time. Many current technologies offer disposable translations from sign language to text or vice versa, but do not provide integrated two-way communication systems. The aim of our study is to overcome these limitations by designing a comprehensive solution that allows for smooth interaction between sign language users and non-users in real-world scenarios. The recognition module uses CNNs to recognize hand gestures related to sign language and convert them into English text. This function allows those who communicate effectively with non signed voice users by using sign language to convert gestures into real-time readable text. The CNN model is trained with a variety of data records with sign language gestures to ensure high accuracy and robustness in a variety of lighting conditions, hand positions and user variations. Converts voice audio inputs and their corresponding sign language gestures. This feature is particularly advantageous for non-essential voice users who want to communicate with people who rely on sign language. By using deep learning models that include NLP techniques (natural language processing), the system processes input text or language and generates accurate representations of visual sign language. The integration of speech recognition provides even greater accessibility, allowing you to convert spoken language into sign language without the need for manual input. A variety of environments, including educational institutions, employment, medical facilities, and public service centers. By using deep learning and computer vision technology, our research contributes to continuous efforts to improve the inclusion and accessibility of hearing impairment and hearing loss.

Citations

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IJARIIE Ritesh Kiran Deore, Shivam Dadasaheb Gavhane, Naeem Rajjak Sayyad, Sanket Rajendra Zende, and Vidya Vinod Jagtap. "Sign Language Generation and Detection using Deep Learning" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 1 2025 Page 1338-1343
MLA Ritesh Kiran Deore, Shivam Dadasaheb Gavhane, Naeem Rajjak Sayyad, Sanket Rajendra Zende, and Vidya Vinod Jagtap. "Sign Language Generation and Detection using Deep Learning." International Journal Of Advance Research And Innovative Ideas In Education 11.1(2025) : 1338-1343.
APA Ritesh Kiran Deore, Shivam Dadasaheb Gavhane, Naeem Rajjak Sayyad, Sanket Rajendra Zende, & Vidya Vinod Jagtap. (2025). Sign Language Generation and Detection using Deep Learning. International Journal Of Advance Research And Innovative Ideas In Education, 11(1), 1338-1343.
Chicago Ritesh Kiran Deore, Shivam Dadasaheb Gavhane, Naeem Rajjak Sayyad, Sanket Rajendra Zende, and Vidya Vinod Jagtap. "Sign Language Generation and Detection using Deep Learning." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 1 (2025) : 1338-1343.
Oxford Ritesh Kiran Deore, Shivam Dadasaheb Gavhane, Naeem Rajjak Sayyad, Sanket Rajendra Zende, and Vidya Vinod Jagtap. 'Sign Language Generation and Detection using Deep Learning', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 1, 2025, p. 1338-1343. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Sign_Language_Generation_and_Detection_using_Deep_Learning_ijariie25805.pdf (Accessed : ).
Harvard Ritesh Kiran Deore, Shivam Dadasaheb Gavhane, Naeem Rajjak Sayyad, Sanket Rajendra Zende, and Vidya Vinod Jagtap. (2025) 'Sign Language Generation and Detection using Deep Learning', International Journal Of Advance Research And Innovative Ideas In Education, 11(1), pp. 1338-1343IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Sign_Language_Generation_and_Detection_using_Deep_Learning_ijariie25805.pdf (Accessed : )
IEEE Ritesh Kiran Deore, Shivam Dadasaheb Gavhane, Naeem Rajjak Sayyad, Sanket Rajendra Zende, and Vidya Vinod Jagtap, "Sign Language Generation and Detection using Deep Learning," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 1, pp. 1338-1343, Jan-Feb 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/Sign_Language_Generation_and_Detection_using_Deep_Learning_ijariie25805.pdf [Accessed : ].
Turabian Ritesh Kiran Deore, Shivam Dadasaheb Gavhane, Naeem Rajjak Sayyad, Sanket Rajendra Zende, and Vidya Vinod Jagtap. "Sign Language Generation and Detection using Deep Learning." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 1 ().
Vancouver Ritesh Kiran Deore, Shivam Dadasaheb Gavhane, Naeem Rajjak Sayyad, Sanket Rajendra Zende, and Vidya Vinod Jagtap. Sign Language Generation and Detection using Deep Learning. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(1) : 1338-1343. Available from: https://ijariie.com/AdminUploadPdf/Sign_Language_Generation_and_Detection_using_Deep_Learning_ijariie25805.pdf
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