实时翻译孟加拉手语，准确率达94%
Real-time Bangla Sign Language Translator
- 用MediaPipe提取关键点，LSTM建模时序特征
- 计算机视觉实现手语实时识别，准确率94%
- 适合聋哑人群体沟通辅助，开发门槛低
人类通过多种有含义的手势进行交流，手语是其中重要形式。孟加拉手语翻译（BSLT）旨在为聋哑群体弥合沟通鸿沟。本研究采用MediaPipe Holistic获取身体关键点，利用LSTM架构进行数据训练，并结合计算机视觉技术实现实时手语检测，整体识别准确率达到94%。关键词：循环神经网络、LSTM、计算机视觉、孟加拉字体。
原文摘要 · Abstract (English)
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.
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