Research Article
Real-Time Sign Language to text Translation using Deep Learning: A Comparative study of LSTM and 3D CNN
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 55 |
| Published: November 2025 |
| Authors: Anvay Anturkar, Anushka Khot, Ayush Andure, Aniruddha Ghosh, Anvit Magadum, Anvay Bahadur, Madhumati Pol |
10.5120/ijca2025925946
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Anvay Anturkar, Anushka Khot, Ayush Andure, Aniruddha Ghosh, Anvit Magadum, Anvay Bahadur, Madhumati Pol . Real-Time Sign Language to text Translation using Deep Learning: A Comparative study of LSTM and 3D CNN. International Journal of Computer Applications. 187, 55 (November 2025), 31-35. DOI=10.5120/ijca2025925946
@article{ 10.5120/ijca2025925946,
author = { Anvay Anturkar,Anushka Khot,Ayush Andure,Aniruddha Ghosh,Anvit Magadum,Anvay Bahadur,Madhumati Pol },
title = { Real-Time Sign Language to text Translation using Deep Learning: A Comparative study of LSTM and 3D CNN },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 187 },
number = { 55 },
pages = { 31-35 },
doi = { 10.5120/ijca2025925946 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Anvay Anturkar
%A Anushka Khot
%A Ayush Andure
%A Aniruddha Ghosh
%A Anvit Magadum
%A Anvay Bahadur
%A Madhumati Pol
%T Real-Time Sign Language to text Translation using Deep Learning: A Comparative study of LSTM and 3D CNN%T
%J International Journal of Computer Applications
%V 187
%N 55
%P 31-35
%R 10.5120/ijca2025925946
%I Foundation of Computer Science (FCS), NY, USA
Abstract
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. Both architectures were evaluated 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 CNN-LSTM 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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