This project aims to detect and recognize American Sign Language (ASL) alphabet signs in images and real-time video using deep learning and computer vision techniques. It leverages the Ultralytics YOLOv8 object detection framework and Streamlit for building an interactive web application.

• Object detection and recognition of ASL alphabet signs
• Support for both image-based and real-time webcam-based detection
• User-friendly web interface for easy interaction
• Display of bounding boxes and class labels on detected signs

• Python 3.11
• Streamlit
• Ultralytics YOLO
• OpenCV
• PIL
• Docker (optional)

1. Clone the repository:

[email protected]:neevaiti/US_sign_language_vision.git

2. Install the required Python packages:

pip install -r requirements.txt

1. Run the Streamlit app:

streamlit run main.py

2. Access the application via the provided URL in the terminal.

3. Select the desired option ("Image" or "Camera") from the sidebar.

4. Follow the instructions for image-based or webcam-based detection.

Alternatively, you can run the application using Docker for easier setup and deployment. Here's how:

1. Pull the Docker image from the Docker Hub:

docker pull neevaiti/asl-detect-app:v1.0

2. Run the Docker container:

docker run -p 80:80 neevaiti/asl-detect-app:v1.0

3. Access the application by opening your web browser and navigating to http://localhost:8501.

• You can adjust the confidence threshold and IOU values in the code to change the detection accuracy and speed.
• To modify the appearance of bounding boxes and class labels, you can change the colors and text properties in the transform and draw_preds methods of the VideoTransformer class.

This project is licensed under the MIT License.

• Ultralytics YOLOv8
• Streamlit
• OpenCV
• PIL
• Docker
• Docker model image
• Dataset

• @Olaffson
• @neevaiti