Real-Time Sign Language to text Translation using Deep Learning: A Comparative study of LSTM and 3D CNN
It addresses the problem of developing assistive technologies for sign language users by providing benchmarks for trade-offs between accuracy and real-time performance, though it is incremental as it compares existing methods.
This study tackled real-time American Sign Language recognition by comparing 3D CNNs and LSTMs, finding that 3D CNNs achieved 92.4% accuracy but with 3.2% higher processing time per frame than LSTMs at 86.7% accuracy.
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.