GestureSpeak

An efficient path to communicate with Sign Language for people needed Powered by machine intelligence.

Table of Contents

• Introduction
• Features
• Usage
• Contributor
• License
• Acknowledgements

Introduction

Our approach leverages n-gram modeling and Knowledge Distillation techniques to enhance performance and accuracy. The system integrates these methodologies to capture the temporal depen- dencies between gestures and reduce the computational burden typically associated with deep learning models. We utilize variants of EfficientNetv2 and MobileNetV3 architectures, pre-trained on the ImageNet dataset, to achieve a balance between efficiency and accuracy.

Features

• Real-time Translation: Converts sign language gestures into text or spoken words in real-time.
• Fast Speed: With the usage of EfficientNet and Knowledge Distillation, our system can be faster than traditional recognition systems with CNNs.
• High Accuracy: Advanced machine learning algorithms ensure high accuracy in gesture recognition.
• American language Support: Focus on American Sign Languages of Alphabets like, A, B, C, etc.

Usage

To set up GestureSpeak locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/YapWH/Understand-What-You-See.git
   cd GestureSpeak
   

2. Validation:

   python real-time.py
   

Contributor

License

This project is licensed under the MIT License. See the LICENSE file for more details.