The Sign Language Detection Project is designed to automatically interpret sign language gestures and translate them into corresponding letters or numbers in real-time. This project includes two main Python scripts, build_model.py and test_model.py, making it easy to build and deploy a sign language recognition model.

• Hand sign photos have been collected and transformed into CSV files.
• The dataset has been divided into training and testing subsets for model evaluation.

• build_model.py reads and normalizes the dataset, and generates addition data from current dataset.
• A deep learning model is constructed, trained, and saved in the "Resources" folder.

• test_model.py is used to test and deploy the model for real-time sign language detection.
• The script accesses the camera feed to recognize and interpret signs.

Ensure you have the following dependencies installed:

• Python (3.x)
• numpy
• pandas
• tensorflow (or keras with tensorflow backend)
• scikit-learn
• opencv-contrib-python

You can install these dependencies manually or use the provided signlanguage.sh script for automated setup.

To run the project, simply execute the signlanguage.sh script. This script automates virtual environment setup, dependency installation, and model execution. It simplifies the process of running the sign language detection model with real-time camera input.

Firstly clone the repository

git clone [email protected]:Krakenmaregit/Sign-Language-Interpretor.git

To provide execution permission to the script, use:

chmod u+x signlanguage.sh

Lastly execute the script

./signlanguage.sh

If you encounter issues or have suggestions for improvements, please feel free to open an issue in the project repository. Contributions and enhancements are also welcome through pull requests.