arxiv:2506.11154

SLRNet: A Real-Time LSTM-Based Sign Language Recognition System

Published on Jun 11, 2025

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Abstract

A real-time sign language recognition system using MediaPipe Holistic and LSTM networks achieves 86.7% validation accuracy for ASL alphabet and functional word classification from webcam video streams.

AI-generated summary

Sign Language Recognition (SLR) plays a crucial role in bridging the communication gap between the hearing-impaired community and society. This paper introduces SLRNet, a real-time webcam-based ASL recognition system using MediaPipe Holistic and Long Short-Term Memory (LSTM) networks. The model processes video streams to recognize both ASL alphabet letters and functional words. With a validation accuracy of 86.7%, SLRNet demonstrates the feasibility of inclusive, hardware-independent gesture recognition.

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