CMPM17-Final-ASLDecoder

This project is a work-in-progress final assignment for the CMPM 17 class. It involves using a Convolutional Neural Network (CNN) for American Sign Language (ASL) recognition. The goal is to build an ASL decoder that can process live video input and classify hand signs using a trained deep learning model.

🚧 Project Status: In Progress 🚧

The project is actively being developed in app.py. The authors are currently working on:

• Implementing live video capture for real-time ASL detection.
• Improving the CNN model for better accuracy.
• Integrating the trained model with a PyQt6-based GUI.

📂 Project Structure

📦 CMPM17-Final-ASLDecoder ┣ 📂 Dataset/ ┃ ┗ 📂 asl_alphabet_train/ # ASL training dataset ┣ 📜 app.py # Main application file (WIP) ┣ 📜 model.py # CNN model and training script ┣ 📜 requirements.txt # Dependencies ┗ 📜 README.md # Project documentation

🛠 Dependencies

To run this project, you need the following dependencies:

• Python 3.x
• PyQt6
• PySide6
• Torch & Torchvision
• NumPy
• OpenCV

Dataset:

We are using the ASL Alphabet dataset from Kaggle:
Download the dataset here

🔧 Installation & Setup

1. Clone the repository:

   git clone https://github.com/your-repo-name/CMPM17-Final-ASLDecoder.git
   cd CMPM17-Final-ASLDecoder

2. Install dependencies:

   pip install -r requirements.txt

3. Download and extract the dataset inside the Dataset/ folder.

4. Run the application:

python app.py

🖥 Current Features

• CNN model for ASL recognition.
• Data augmentation for improved training.
• Real-time ASL detection from video input (In Progress).
• GUI for user interaction (Planned).