American Sign Language Detection

Abstract

1. Introduction

This ASL Detector is a cutting-edge AI-powered application that uses computer vision and deep learning to recognize and classify American Sign Language (ASL) characters in real-time. This application utilizes the device's camera to capture hand landmarks and coordinates, which are then processed by a deep learning model to identify the corresponding ASL character.

2. Objective

A real-time American Sign Language (ASL) detection system using computer vision and deep learning. This project uses a combination of OpenCV, MediaPipe, and TensorFlow to detect and classify ASL hand signs from camera input. The system can recognize a wide range of ASL characters, and can be used to facilitate communication for sign language users.

This project demonstrates how Jupyter Notebook, Computer Vision, Deep Learning can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• Real-time ASL detection using the device's camera.
• Accurate classification of ASL characters using a deep learning model.
• Hand landmark tracking for precise gesture recognition.
• Support for a wide range of ASL characters and phrases.
• High accuracy and robustness in varying lighting conditions.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/AkramOM606/American-Sign-Language-Detection.git
cd American-Sign-Language-Detection

1. Clone the Repository:
2. Install Dependencies:
3. Run the Application:

git clone https://github.com/AkramOM606/American-Sign-Language-Detection.git
cd American-Sign-Language-Detection

pip install -r requirements.txt

python main.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by AkramOM606 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.