Sign Language Recognition Project

Project Overview

The Sign Language Recognition project is designed to recognize and interpret sign language gestures using hand landmarks captured via computer vision. It combines machine learning techniques with real-time hand tracking to convert hand movements into text, making sign language more accessible and inclusive for communication.

Key Features

• Real-time Hand Tracking: Utilizes the MediaPipe library to track and analyze hand movements captured through a webcam.

• Data Collection: Allows users to contribute to data collection by using the "Sign Language CSV Dataset Collector" application. This tool records landmark points and their components for building a robust sign language dataset.

• SVM Model: Employs a Support Vector Machine (SVM) model for sign language recognition. The model is trained on a dataset of hand landmarks and labels, enabling it to classify hand signs.

• User Interface: Incorporates a user-friendly interface using Streamlit, allowing users to interact with the model and view recognition results in real-time.

Getting Started

Prerequisites

• Python (Python 3.6 or higher)
• Pip

Installation

1. Clone the repository to your local machine.

   git clone https://github.com/yourusername/sign-language-recognition.git

2. Navigate to the project directory.

   cd sign-language-recognition

3. Install the required Python packages.

   pip install -r requirements.txt

4. Run the project.

   streamlit run main.py

Usage

1. Start the project by running the Streamlit application.
2. Use the "Sign Language CSV Dataset Collector" page to collect data and create a dataset.
3. Interact with the SVM model for real-time sign language recognition.
4. Explore the features and functionalities provided by the project.

Roadmap

Future enhancements for the Sign Language Recognition project include:

• Extending Gesture Vocabulary: Expanding the dataset and model to recognize a wider range of sign language gestures and expressions.
• Speech Output: Implementing a speech synthesis feature to audibly interpret sign language gestures.
• Mobile Application: Developing a mobile application for on-the-go sign language recognition.
• Multilingual Support: Adding support for various sign languages and languages for improved accessibility on a global scale.