对比3D CNN与LSTM在实时手语识别中的表现，为辅助技术选型提供依据。
Real-Time Sign Language to text Translation using Deep Learning: A Comparative study of LSTM and 3D CNN
- 用3D CNN捕捉视频时空特征，用LSTM建模时序依赖
- 3D CNN准确率达92.4%，但每帧多耗3.2%处理时间
- 混合模型表现均衡，适合边缘计算场景
本研究评估3D卷积神经网络（3D CNN）和长短期记忆网络（LSTM）在实时美国手语（ASL）识别中的性能。尽管3D CNN擅长从视频序列中提取时空特征，而LSTM则专长于建模序列数据的时序依赖性。我们在包含50类共1,200个ASL手势的数据集上比较了两种架构，在相同训练条件下评估其准确率、计算效率和延迟。实验结果表明，3D CNN达到92.4%的识别准确率，但每帧处理时间比LSTM多3.2%；LSTM保持86.7%准确率，资源消耗显著更低。混合3D CNN-LSTM模型表现出良好性能，说明实际应用中需根据场景选择合适架构。该工作为辅助技术开发提供了专业基准，突出了在边缘计算环境下识别精度与实时性之间的权衡。
原文摘要 · Abstract (English)
This study investigates the performance of 3D Convolutional Neural Networks (3D CNNs) and Long Short-Term Memory (LSTM) networks for real-time American Sign Language (ASL) recognition. Though 3D CNNs are good at spatiotemporal feature extraction from video sequences, LSTMs are optimized for modeling temporal dependencies in sequential data. We evaluate both architectures on a dataset containing 1,200 ASL signs across 50 classes, comparing their accuracy, computational efficiency, and latency under similar training conditions. Experimental results demonstrate that 3D CNNs achieve 92.4% recognition accuracy but require 3.2% more processing time per frame compared to LSTMs, which maintain 86.7% accuracy with significantly lower resource consumption. The hybrid 3D CNNLSTM model shows decent performance, which suggests that context-dependent architecture selection is crucial for practical implementation.This project provides professional benchmarks for developing assistive technologies, highlighting trade-offs between recognition precision and real-time operational requirements in edge computing environments.
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