Lip reading using TensorFlow, OpenCV, and Keras involves training a deep learning model to recognize spoken words by analyzing lip movements from video frames. The process starts with OpenCV for capturing and preprocessing video frames, focusing on the speaker’s lips. These frames are then fed into a neural network built using Keras and TensorFlow.
The model architecture typically includes Conv3D layers to extract spatio-temporal features from the video, followed by MaxPool3D for downsampling. LSTM layers, especially in a Bidirectional setup, are used to capture temporal dependencies in the lip movements. Dense layers with Activation functions handle classification, while Dropout and SpatialDropout3D prevent overfitting. BatchNormalization ensures stable learning. TimeDistributed and Flatten layers help in handling sequential data, and Adam is used as the optimizer for training.