This project focuses on implementing Lip Reading using 3D convolution with TensorFlow, Keras, and OpenCV. The system is designed to recognize English language lip movements from video data.
Before running the Lip Reader project, make sure to install the required libraries. You can install them using the following command:
pip install opencv-python tensorflow numpy matplotlib imageiopip install opencv-python tensorflow numpy matplotlib imageio
OpenCV (cv2): Used for image and video processing. TensorFlow: Deep learning framework for building and training models. NumPy: Library for numerical operations, particularly useful for handling arrays and matrices. Matplotlib: Used for creating visualizations and plots. ImageIO: Library for reading and writing image data.
1.Clone the repository:
git clone https://github.com/ahmedanwar123/LipReader.git
cd LipReader2.Install dependencies:
pip install -r requirements.txt1.Open the Jupyter Notebook:
jupyter notebook LipReader.ipynbThe lip reading model architecture utilizes a 3D convolutional neural network (CNN) for feature extraction from video frames. The model is trained on a dataset of labeled lip movement sequences.
Preparing training data by organizing video sequences and corresponding labels. The dataset should include English language speakers showcasing various lip movements.
To train the model, follow these steps:
Organize your dataset. Run the training script in the notebook. Evaluation Evaluate the trained model on a separate test set using the provided evaluation script in the notebook.
streamlit run streamlitapp.pyLanguage Expansion: Include support for Arabic language lip reading. Enhanced Model: Experiment with more advanced 3D convolutional architectures. Real-time Processing: Implement real-time lip reading capabilities. Multilingual Support: Extend language support to other languages.# Lip Reader
This project focuses on implementing Lip Reading using 3D convolution with TensorFlow, Keras, and OpenCV. The system is designed to recognize English language lip movements from video data.
Before running the Lip Reader project, make sure to install the required libraries. You can install them using the following command:
pip install opencv-python tensorflow numpy matplotlib imageiopip install opencv-python tensorflow numpy matplotlib imageio
OpenCV (cv2): Used for image and video processing. TensorFlow: Deep learning framework for building and training models. NumPy: Library for numerical operations, particularly useful for handling arrays and matrices. Matplotlib: Used for creating visualizations and plots. ImageIO: Library for reading and writing image data.
1.Clone the repository:
git clone https://github.com/ahmedanwar123/LipReader.git
cd LipReader2.Install dependencies:
pip install -r requirements.txt1.Open the Jupyter Notebook:
jupyter notebook LipReader.ipynbThe lip reading model architecture utilizes a 3D convolutional neural network (CNN) for feature extraction from video frames. The model is trained on a dataset of labeled lip movement sequences.
Prepare your training data by organizing video sequences and corresponding labels. The dataset should include English language speakers showcasing various lip movements.
To train the model, follow these steps:
Organize your dataset. Run the training script in the notebook. Evaluation Evaluate the trained model on a separate test set using the provided evaluation script in the notebook.
streamlit run streamlitapp.py- Language Expansion: Include support for Arabic language lip reading.
- Enhanced Model: Experiment with more advanced 3D convolutional architectures.
- Real-time Processing: Implement real-time lip reading capabilities.
- Multilingual Support: Extend language support to other languages.