Real-time Bangla Sign Language Translator
This addresses a domain-specific problem for the Bangla-speaking deaf and mute community, with incremental improvements in real-time detection.
The paper tackles real-time translation of Bangla Sign Language to bridge communication gaps for the deaf and mute community, achieving 94% accuracy using Mediapipe Holistic, LSTM, and computer vision.
The human body communicates through various meaningful gestures, with sign language using hands being a prominent example. Bangla Sign Language Translation (BSLT) aims to bridge communication gaps for the deaf and mute community. Our approach involves using Mediapipe Holistic to gather key points, LSTM architecture for data training, and Computer Vision for realtime sign language detection with an accuracy of 94%. Keywords=Recurrent Neural Network, LSTM, Computer Vision, Bangla font.