Year: 2025
Type: article
Abstract: : In order to bridge the communication gap between the general public and people who are difficult of hearing, sign detection is crucial. This study presents a novel approach to sign language identification that blends action detection with deep learning models based on the Python-based LSTM algorithm. Accurately recognizing and decoding sign language movements in real time is the goal of the suggested system. The goal of this study is to use deep learning techniques, namely the LSTM architecture, to effectively recognize sign language motions and record temporal connections. A large dataset of various sign language gestures has been gathered and examined with the goal to train and evaluate the accuracy of the LSTM model.The processes in the process of sign language detection pipeline include media acquisition, preprocessing, feature extraction, and model training. During the pre-processing stage, the acquired video data is separated into individual frames, and various image processing techniques are applied to enhance the quality and remove noise. After that, the preprocessed frames undergo robust feature extraction using techniques like optical flow or machine learning-based feature extraction. The LSTM model is then trained with the recovered information to learn the time-dependent properties of sign language gestures. Transfer training is also being researched as a way to use pre-trained models on large action recognition datasets. The effectiveness of the model that was trained is assessed using a variety of measures, including recall, accuracy, precision, and F1-score. The LSTM-based deep learning model's exceptional accuracy rate shows that it can handle the temporal component of sign language. The system's real-time performance makes it suitable for a wide range of applications, such as real-time interpretation tools or assistive devices for those with hearing impairments
Author: Punit Mittal
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410205683