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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).