Gesture Language Translator

Abstract

The \"Sign-Wave\" project will let people who can see and hear their environment communicate and comprehend one another. People with hearing impairments still have restricted access to digital platforms, despite the fact that sign language is their primary means of communication. By offering real-time language identification and translation, the project seeks to improve computer vision and machine learning for the deaf and blind communities. We aim to close the gap between sign language users and non-signers and enable smooth communication in a variety of settings by creating reliable algorithms that can precisely identify and interpret sign language motions. The project\'s concept is to capture sign language using video input and then analyze it using a variety of computer techniques.

Introduction

Problem Overview

By 2050, nearly 900 million people—primarily in low- and middle-income countries—are expected to have hearing impairments, up from 466 million today (World Wellbeing Association). Sign language is a vital mode of communication for the deaf and mute, but its manual interpretation is slow and error-prone. The rise of telemedicine and digital communication further increases the need for real-time, accessible sign language translation systems.

2. Solution Overview

The Sign-Wave Project aims to create an affordable, real-time gesture recognition system that converts American Sign Language (ASL) gestures into text and speech using CNN-based machine learning models and web technologies like React, Node.js, and Python (Flask).

3. Background

Gesture recognition systems using computer vision and machine learning have advanced significantly. These systems are crucial in enabling better communication for those with speech and hearing impairments, especially as reliance on digital interfaces increases.

4. Motivation

The project's goal is to make communication inclusive by developing a real-time ASL-to-speech and text translator that can run on common devices like smartphones and laptops. It supports the belief that communication is a fundamental human right.

5. Literature Review Highlights

• Systems using CNNs and RNNs show high accuracy (up to 99.7%) in gesture recognition across different sign languages.

• Vision-based models (more practical than sensor-based ones) dominate research due to accessibility and lower cost.

• Real-time processing, high accuracy, and integration with mobile platforms are key trends.

6. Experiments Conducted

• Data Collection: Captured and preprocessed ASL gesture images using webcams.

• Model Training: Used Teachable Machine and TensorFlow for CNN model training.

• System Integration: Combined gesture recognition with Flask backend and React front-end. Used Google TTS for speech output.

• User Study: Tested usability with deaf and mute participants to assess translation quality and system responsiveness.

7. Methodology

• Tech Stack: MERN stack (MongoDB, Express.js, React.js, Node.js) with Python for ML backend.

• Front-End: Captures video, extracts frames, and sends them for analysis.

• Back-End: Receives image data, invokes Python CNN model, maps gestures to text, and uses TTS to produce audio.

• CNN Model: Trained on 48x48 grayscale ASL gesture images using Keras and OpenCV. Predictions are mapped to ASL characters and converted into speech using pyttsx3.

8. System Workflow

1. Video Capture: Live feed from webcam.

2. Frame Extraction: Periodic capture of image frames.

3. Gesture Prediction: CNN processes frame and identifies gesture.

4. Text & Speech Output: Recognized gestures are displayed as text and spoken via TTS engine.

9. Key Features

• Real-time gesture recognition and audio conversion.

• Compatibility with low-cost devices.

• Web-based user interface with seamless front-end and back-end communication.

• High gesture detection accuracy and user satisfaction.

Conclusion

The SIGN-WAVE Venture provides a positive step forward in the field of gesture translation communication by using sign wave adjustments to accurately replicate and comprehend gesture-based communication movements. The task\'s ability to accurately manipulate waves to decipher confusing movements demonstrates a creative and sensible approach to removing barriers to correspondence for the deaf. Its versatility and potential for broad application are shown by its ability to function well in various gesture-based interactions. Later on, there are many chances to work on the framework\'s capabilities. Short interpretation and improved correspondence will be effective when continual handling is the main focus. Consider expanding the motion library to include new indicators and types to further improve the framework\'s usability and inclusivity.

References

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Copyright

Copyright © 2025 Miss. Krishna Bhatt, Mr. Dev Patel, Miss. Isha Bhanvadia, Mr. Kenil Butani, Miss. Ruchita Vachhani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.