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Real-Time Sign Language Recognition Framework Utilizing 3D Pose Estimation and Recurrent Neural Networks

Author

Listed:
  • I Pawan Kumar
  • K Rupesh Reddy
  • G Sai Kumar
  • Nakka Rajasekhar
  • Nallabothula Vinod Kumar
  • Famida Begum

Abstract

Sign language serves as the primary mode of communication for the hearing and speech-impaired community. However, a significant communication gap exists between sign language users and non-signers, necessitating the development of automated translation systems. This paper presents a robust Sign Language Recognition (SLR) system designed to interpret isolated gestures from video sequences. The proposed methodology leverages Google MediaPipe for efficient, lightweight 3D pose extraction, capturing spatial dependencies of hand and body keypoints. These temporal sequences are subsequently processed using Long Short-Term Memory (LSTM) networks to classify gestures. Experimental results utilizing a subset of the WLASL dataset demonstrate that the proposed skeleton-based approach achieves high recognition accuracy while maintaining low computational complexity, making it suitable for deployment in real-time accessibility applications.

Suggested Citation

  • I Pawan Kumar & K Rupesh Reddy & G Sai Kumar & Nakka Rajasekhar & Nallabothula Vinod Kumar & Famida Begum, 2026. "Real-Time Sign Language Recognition Framework Utilizing 3D Pose Estimation and Recurrent Neural Networks," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 12(3), pages 662-672, June.
    • Handle: RePEc:jbh:ijsrcs:v12:y2026:i3:id:2071
    DOI: 10.32628/CSEIT26123364
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT26123364
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