SIGN LANGUAGE TO VOICE TRANSLATOR
AUTHORS:
Mentor
Dr. Ajay Kumar Singh,
Affiliation
Dept of CSE, Jain (Deemed-to-be University)
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Abstract
One area of assistive technology that is gaining popularity is its capacity to facilitate communication between people with hearing impairments and the general public. This research introduces a real-time sign language detection system that uses a single webcam to recognize the alphabet in American Sign Language (ASL) and interpret numerical gestures. Based on hand landmarks recorded by MediaPipe, the system recognizes ASL alphabets with high accuracy and recognizes digits from 1 to 10 using deep learning, computer vision, and language processing algorithms. The suggested solution combines OpenCV and MediaPipe for landmark tracking, pyttsx3 for speech feedback, and a user-friendly graphical user interface created using Tkinter. TensorFlow is used to train the alphabet identification model, while landmark distance computations and geometric logic is used to distinguish numerical movements. Because this hybrid approach guarantees real-time speed and usability, the solution is feasible for applications that are focused on accessibility, education, and assistive technology.
Keywords
American Sign Language Mediapipe OpenCV Sign -to-text text-to-speech TKinter
Article Information
PUBLICATION DETAILS
Review Type:
0
License:
CC BY-NC 4.0
Volume/Issue:
Volume Volume 10, Issue 01
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Feb 21 2026
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Praniksha, B. S. P. ,. D. R. ,. S. R. J. ,. M. (2026). Sign Language to Voice Translator. International Journal of Science, Strategic Management and Technology, Volume 10(01). https://doi.org/10.55041/ijsmt.v2i2.069
Praniksha, B.. "Sign Language to Voice Translator." International Journal of Science, Strategic Management and Technology, vol. Volume 10, no. 01, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i2.069.
Praniksha, B.. "Sign Language to Voice Translator." International Journal of Science, Strategic Management and Technology Volume 10, no. 01 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i2.069.
References
[1] B. A. Boobal, C. A. Reddy, L. J. J. Loyola, C. C. K. Reddy, and C. B. V. S. Rohith, “Real-Time Sign Language and Audio Conversion Using AI,” 2024 International Conference on Communication, Control, and Intelligent Systems (CCIS), Virudhunagar, India, 2024, Doi: 10.1109/CCIS63231.2024.10932061.
[2] O. Kumar C.U., B. V., K. P. K. Devan, A. Srinivas, R. P. Renukadevi, and K. R. Krithiga, “Real Time Detection and Conversion of Gestures to Text and Speech to Sign System,” 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2022, pp. 1–6, doi: 10.1109/ICESC54411.2022.9885562.
[3] H. Orovwode, I. D. Oduntan, and J. Abubakar, “Development of a Sign Language Recognition System Using Machine Learning,” 2023 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Ota, Nigeria, 2023, pp. 1–6, doi: 10.1109/ICABCD59051.2023.10220456.
[4] S. Manzoor, Z. Abbas, G. Chhabra, K. Kaushik, M. Zehra, Z. Haider, and I. U. Khan, “Voice of Hearing and Speech Impaired People,” 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), Karachi, Pakistan, 2024, pp. 1–6, doi: 10.1109/IC3SE62002.2024.10593248.
[5] V. R. Swaminathan, S. Agrrawal, and C. Santhanakrishnan, “Gesture Recognition System Using Convoluted Neural Networks and Transfer Learning,” 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), Kattankulathur, India, 2022, pp.1–6, doi:
10.1109/ICATIECE56365.2022.10046734.
[6] A. Deshmukh, A. Machindar, S. Lale, and P. Kasambe, “Enhancing Communication for the Hearing Impaired: A Real-Time Speech to Sign LanguageConverter,”.1–6, doi: 10.1109/WPMC63271.2024.10863135.
[7] A. Rustagi, S. Shaina, and N. Singh, “American and Indian Sign Language Translation using Convolutional Neural Networks,” Noida, India, 2021, pp.1–6, doi: 10.1109/SPIN52536.2021.9566105.
[8] S. Ikram and N. Dhanda, “American Sign Language Recognition using Convolutional Neural Network,” 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Kuala Lumpur, Malaysia, Sep. 24-26, 2021, pp. 1–6, doi: 10.1109/GUCON50781.2021.9573782.
[9] N. S. M. Salleh, J. Jais, L. Mazalan, R. Ismail, S. Yussof, A. Ahmad, A. Anuar, and D. Mohamad, “Sign Language to Voice Recognition: Hand Detection Techniques for Vision-Based Approach,” Univ. Tenaga Nasional, College of Information Technology, Universiti Tenaga Nasional, Kajang, Selangor, Malaysia, and Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, Johor Darul Takzim, Malaysia.
[10] S. J, M. Vespa, S. R. M, and K. R, “Sign Language Translation and Voice Impairment Support System using Deep Learning,” 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), 2024. DOI: 10.1109/ICDICI62993.2024.10810957.
[2] O. Kumar C.U., B. V., K. P. K. Devan, A. Srinivas, R. P. Renukadevi, and K. R. Krithiga, “Real Time Detection and Conversion of Gestures to Text and Speech to Sign System,” 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2022, pp. 1–6, doi: 10.1109/ICESC54411.2022.9885562.
[3] H. Orovwode, I. D. Oduntan, and J. Abubakar, “Development of a Sign Language Recognition System Using Machine Learning,” 2023 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Ota, Nigeria, 2023, pp. 1–6, doi: 10.1109/ICABCD59051.2023.10220456.
[4] S. Manzoor, Z. Abbas, G. Chhabra, K. Kaushik, M. Zehra, Z. Haider, and I. U. Khan, “Voice of Hearing and Speech Impaired People,” 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), Karachi, Pakistan, 2024, pp. 1–6, doi: 10.1109/IC3SE62002.2024.10593248.
[5] V. R. Swaminathan, S. Agrrawal, and C. Santhanakrishnan, “Gesture Recognition System Using Convoluted Neural Networks and Transfer Learning,” 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), Kattankulathur, India, 2022, pp.1–6, doi:
10.1109/ICATIECE56365.2022.10046734.
[6] A. Deshmukh, A. Machindar, S. Lale, and P. Kasambe, “Enhancing Communication for the Hearing Impaired: A Real-Time Speech to Sign LanguageConverter,”.1–6, doi: 10.1109/WPMC63271.2024.10863135.
[7] A. Rustagi, S. Shaina, and N. Singh, “American and Indian Sign Language Translation using Convolutional Neural Networks,” Noida, India, 2021, pp.1–6, doi: 10.1109/SPIN52536.2021.9566105.
[8] S. Ikram and N. Dhanda, “American Sign Language Recognition using Convolutional Neural Network,” 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Kuala Lumpur, Malaysia, Sep. 24-26, 2021, pp. 1–6, doi: 10.1109/GUCON50781.2021.9573782.
[9] N. S. M. Salleh, J. Jais, L. Mazalan, R. Ismail, S. Yussof, A. Ahmad, A. Anuar, and D. Mohamad, “Sign Language to Voice Recognition: Hand Detection Techniques for Vision-Based Approach,” Univ. Tenaga Nasional, College of Information Technology, Universiti Tenaga Nasional, Kajang, Selangor, Malaysia, and Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, Johor Darul Takzim, Malaysia.
[10] S. J, M. Vespa, S. R. M, and K. R, “Sign Language Translation and Voice Impairment Support System using Deep Learning,” 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), 2024. DOI: 10.1109/ICDICI62993.2024.10810957.
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