Sign Language Detection using Deep Learning 🤟🧠

This project implements a Sign Language Detection system using Deep Learning.
A pre-trained model is provided to recognize three different hand sign indentations, and predictions can be generated by following the steps outlined in the Jupyter Notebook.

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📁 Project Structure

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├── Sign language detection.ipynb   # Main Jupyter Notebook
├── MP_data/                        # Trained model data (3 sign indentations)
├── README.md                       # Project documentation

📌 Project Overview

The notebook demonstrates:

• Loading a pre-trained deep learning model
• Processing input data for sign language recognition
• Predicting sign language classes using the trained model
• Visualizing prediction results

The model has already been trained, and this project focuses primarily on inference and evaluation.

🧰 Software Requirements

Programming Language

• Python 3.7+

Required Libraries

pip install numpy pandas matplotlib tensorflow keras opencv-python

It is recommended to run the project inside a virtual environment or conda environment.

📊 Model Data (MP_data)

• The folder MP_data contains the trained model files
• The model is trained to recognize three sign language indentations
• Do not modify or delete this folder, as it is required for prediction

🚀 How to Run

1. Clone the repository:

git clone <YOUR_REPOSITORY_URL>
cd <repository_name>

2. Ensure the MP_data folder is present in the project directory
3. Launch Jupyter Notebook:

jupyter notebook

4. Open the notebook:

Sign language detection.ipynb

5. Follow the steps sequentially in the notebook to:

• Load the trained model
• Provide input data
• Predict the sign language result

🧠 Techniques Used

• Deep Learning–based classification
• Pre-trained neural network model
• Feature extraction and inference
• TensorFlow / Keras backend

📜 License

• This project is intended for academic and educational use.
• You are free to modify and extend the notebook for learning purposes.

👤 Author

• Shakthi Bala

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✅ Why this README fits well

• Notebook-centric (not code-heavy)
• Clear explanation of MP_data
• Easy for anyone to reproduce results
• Recruiter & academic friendly

If you want, I can:

• Add a model architecture explanation
• Add a results visualization section
• Convert this into a portfolio-ready ML project
• Align all your READMEs to a consistent personal style

Just tell me 👍