Sign Recognition System

This repository contains code for a real-time sign language recognition system. The system is designed to take sign language gestures as input and convert them into real sentences and audio files using machine learning algorithms.

Getting Started

To get started with the sign recognition system, follow these steps:

1. Run sign_recog_for_further.py to train the data and test it in real-time.
2. Once the model weights are ready, run sign_recog_run.py to import your data and use the sentence_creator_run model to create sentences for the recognized signs.

Usage

Training Data

The training data consists of sign language gestures that you want to recognize. Ensure that your training data covers a wide range of gestures to improve recognition accuracy.

Testing in Real-Time

Use the sign_recog_for_further.py script to test the system in real-time. This script will capture live video input and recognize the gestures using the trained model.

Generating Sentences and Audio Files

After training the model and testing it in real-time, use the sign_recog_run.py script to import your data and generate sentences for the recognized signs. The system will also generate audio files corresponding to the recognized sentences.

Dependencies

• Python 3.x
• Machine Learning Libraries (e.g., TensorFlow, Scikit-learn)
• OpenCV
• NumPy

Contributing

Contributions to improve the sign recognition system are welcome. Feel free to fork this repository, make changes, and submit pull requests.

Acknowledgements

• Special thanks to the contributors and developers of the machine learning libraries used in this project.
• This project was inspired by the need for accessible communication tools for the hearing impaired community.