Project Title

Welcome to the Sign Language Detection Project! This project is designed to facilitate communication for the deaf and hard-of-hearing community by translating sign language into text in real-time. Utilizing advanced machine learning techniques, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, our system interprets video input to recognize and translate sign language gestures accurately.

Key Features Real-Time Translation: Translates sign language into text instantaneously, allowing for seamless communication. Advanced Machine Learning Models: Combines CNNs for spatial feature extraction and LSTMs for temporal dynamics understanding, enhancing accuracy and reliability. Customizable Interface: User-friendly interface that can be tailored to individual preferences and needs. Extensive Training Dataset: Developed using a custom dataset to cover a wide range of signs and ensure robust performance across diverse conditions. Benefits Enhanced Accessibility: Provides a vital communication tool for the deaf and hard-of-hearing, enabling better access to education, services, and social interactions. Learning and Education: Supports learners of sign language by providing real-time feedback and translation. Community Engagement: Bridges the gap between the deaf community and the hearing world, promoting inclusivity. Usage This system is intended for educational purposes, as well as practical application in real-world environments. It can be integrated into various platforms and devices, offering a versatile tool for communication and learning.

Thank you for exploring our project. We hope it serves as a valuable resource in enhancing communication accessibility and fostering connections within and across communities.

Overview

This project contains several Python scripts that collectively handle data collection, processing, model training, and application deployment for a machine learning model. Each script is designed to perform specific functions as part of the larger application workflow.

Files and Descriptions

• CollectDataset.py: Handles the collection and preparation of datasets.
• Data.py: Provides utilities for data handling and processing.
• Functions.py: Contains common functions used across various modules.
• MainApp.py: Serves as the main entry point for the application.
• TrainModel.py: Includes logic for model training and evaluation.

Installation

To run this project, you will need Python installed on your machine. Additionally, install the required dependencies:

pip install -r requirements.txt

Usage

To run the main application, execute the following command:

python MainApp.py

Requirements

Ensure that you have the following dependencies installed:

• OpenCV
• Keras
• MediaPipe
• NumPy
• scikit-learn

These can be installed using the provided requirements.txt.