This project aims to develop a deep learning model for lip reading using Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) cells. Lip reading, also known as automatic speech recognition from lip movements, plays a crucial role in enhancing communication accessibility for individuals with hearing impairments and in noisy environments.

• data/: Contains the dataset used for training and evaluation.
• models/: Stores the trained models and checkpoints.
• notebooks/: Jupyter notebooks for data exploration, model development, and evaluation.
• src/: Source code for data preprocessing, model training, and inference.

• Python (>=3.6)
• TensorFlow (>=2.0)
• OpenCV (>=4.0)
• NumPy (>=1.16)
• Matplotlib (>=3.0)
• ImageIO (>=2.5)

1. Clone the repository:
2. Install the required dependencies
3. 3. Prepare the dataset:

• Place the dataset in the data/ directory.
• Preprocess the video data using the provided scripts.

4. Train the model:

• Run the training script to train the deep learning model on the dataset.

5. Evaluate the model:

• Use the trained model to make predictions on test data and evaluate performance metrics.

6. Fine-tune and experiment with different architectures, hyperparameters, and optimization strategies to improve model performance.

This project was developed by Varun Reddy/Darshan/Arya/Sai as part of University Final Year Project.