American Sign Language Translation into Text and Speech

  • Unique Paper ID: 179725
  • Volume: 11
  • Issue: 12
  • PageNo: 7893-7896
  • Abstract:
  • Sign language is one of the communication ways for the people who have hearing issues. Common people find it a challenge to communicate with deaf people, that’s where this system is useful. This project presents the development of real-time American sign language translation into text and voice. This System captures the hand gestures using a webcam and processes using the Mediapipe and OpenCv Python libraries. These 3D landmark coordinates are then structured into a consistent format and passed to a trained 2D Convolutional Neural Network (CNN) for classification. The model accurately predicts ASL Gestures. The predicted output is displayed as text, with optional conversion to voice, enabling more accessible and seamless communication across hearing boundaries.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{179725,
        author = {Pranjali Kolawale and Pranav Deore and Dr. Disha Gabhane},
        title = {American Sign Language Translation into Text and Speech},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7893-7896},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179725},
        abstract = {Sign language is one of the communication ways for the people who have hearing issues. Common people find it a challenge to communicate with deaf people, that’s where this system is useful. This project presents the development of real-time American sign language translation into text and voice. This System captures the hand gestures using a webcam and processes using the Mediapipe and OpenCv Python libraries. These 3D landmark coordinates are then structured into a consistent format and passed to a trained 2D Convolutional Neural Network (CNN) for classification. The model accurately predicts ASL Gestures. The predicted output is displayed as text, with optional conversion to voice, enabling more accessible and seamless communication across hearing boundaries.},
        keywords = {ASL, CNN, Landmark},
        month = {May},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 7893-7896

American Sign Language Translation into Text and Speech

Related Articles