International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 3
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Real-Time Sign Language Translator

Author(s) Mr. Ansh Raj Mittal, Mr. Satvik Shrivastava, Mr. Bhavya Kumar
Country India
Abstract This paper presents a real-time sign language translation system integrating Google MediaPipe Handslandmark extraction, sequence-anchored coordinate normalization, and a Transformer-based deep learning architecture.The system recognizes fifteen distinct sign language gesture classes—wave,yes, no, stop, wait, yo, good, bad, peace,call_me, promise, up, down, circle, and idle—from a standard webcam without specialized sensors. The Transformerencoder employs four-head multi-head self-attention (key dimension 256), Conv1D feed-forward sublayers, residualconnections, and Layer Normalization to capture long-range temporal dependencies across 60-frame gesture sequences.A rolling buffer and five-frame majority-vote stabilization mechanism suppress prediction noise for stable real-timeoutput. Evaluation on a 27,000-frame dataset yields 100% classification accuracy with precision, recall, and F1-scoreeach equal to 1.0000. The confusion matrix exhibits perfect diagonal dominance with zerointer-class misclassification.Mean end-to-end inference latency is 39.0 ms on CPU-only consumer hardware, confirming practical real-timedeployment suitability.
Keywords Sign Language Recognition, Deep Learning, Transformer Networks, MediaPipe, Gesture Recognition, Computer Vision, Human-Computer Interaction, Real-Time Translation, Multi-Head Attention, Temporal Sequence Classification
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-11
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E-ISSN 2582-2160
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