用LSTM实时识别手语字母和词汇，无需特殊设备。
SLRNet: A Real-Time LSTM-Based Sign Language Recognition System
- 结合MediaPipe与LSTM，从摄像头视频流中实时解析手语
- 验证准确率达86.7%，支持字母与功能词识别
- 适合无障碍沟通场景，硬件要求低，部署便捷
手语识别（SLR）在弥合听障人群与社会之间的沟通鸿沟中发挥着关键作用。本文提出SLRNet，一种基于MediaPipe Holistic与长短期记忆网络（LSTM）的实时、摄像头驱动的美国手语（ASL）识别系统。该模型可处理视频流，实现对ASL字母表字符及常用词汇的识别。在验证集上，系统达到86.7%的准确率，验证了其在无特定硬件依赖条件下的可行性，为包容性手势识别提供了实用方案。
原文摘要 · Abstract (English)
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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