Real-Time ASL Hand Gesture Recognition Using Computer Vision

Abstract

This project implements real-time recognition of American Sign Language (ASL) hand gestures using computer vision and machine learning. It leverages Mediapipe for hand tracking and a Random Forest Classifier for gesture prediction. A user-friendly GUI built with Tkinter facilitates data collection, training, and live inference.

Features

• Real-Time Detection: Recognizes ASL hand gestures using webcam input.
• Data Collection: Streamlined collection of hand gesture images for training.
• Dataset Creation: Automatic conversion of collected data into training-ready datasets.
• Model Training: Easy training of a Random Forest Classifier with a few clicks.
• GUI Interface: Interactive and user-friendly interface built with Tkinter.

How It Works

1. Hand Tracking: Mediapipe is used to detect and track hand landmarks in real-time.
2. Feature Extraction: The relative positions of hand landmarks are extracted for each frame.
3. Machine Learning: A Random Forest Classifier is trained on the collected data to predict ASL letters.
4. Real-Time Inference: The trained model predicts the ASL letter from the webcam feed.

Results

An example of me starting and capturing data for ASL letters A and B:

An example of me creating a dataset and training the classifier:

A live ASL detection demo:

Installation and Usage

Prerequisites

Ensure you have Python 3.7+ installed.

Installation

1. Clone the repository:

   git clone https://github.com/almoamenahmed/ASL-Detection.git
   cd ASL-Detection
   

2. Install Required Dependencies:

   pip install -r requirements.txt

Running the Application

1. Launching the GUI:

   python gui.py

Future Enhancements

• Dynamic Gesture Recognition: Expand to recognize full ASL words and phrases.
• Improved Accuracy: Integrate deep learning models (e.g., CNNs) for enhanced predictions.
• Mobile App: Develop a mobile version of the application for broader accessibility.

Acknowledgements

• Mediapipe: For robust hand tracking.
• Scikit-learn: For machine learning utilities.
• OpenCV: For image and video processing.