Build a Deep Learning Model that can LIP READ using Python and Tensorflow | Full Tutorial

Lip reading, also known as speech-reading, is the ability to understand speech by visually interpreting the movements of the lips, face, and tongue. It is an important skill for people who are deaf or hard of hearing, as well as for applications in human-computer interaction, surveillance, and forensics.

In this tutorial, we will walk through the process of building a deep learning model for lip reading using Python and Tensorflow. We will use a dataset of videos with corresponding transcripts to train our model to recognize and transcribe spoken words based on lip movements.

Prerequisites

In order to follow along with this tutorial, you will need to have the following:

• Python installed on your machine
• Tensorflow and Keras libraries for deep learning
• A dataset of videos with corresponding transcripts for training

Steps

1. Data Preprocessing: We will start by preprocessing the video data, extracting frames, and aligning them with the corresponding transcripts.
2. Feature Extraction: Next, we will extract features from the preprocessed frames, such as optical flow or appearance-based features.
3. Model Building: We will then build a deep learning model using Tensorflow and Keras to train on the extracted features and transcripts.
4. Model Evaluation: We will evaluate the performance of our trained model using a test dataset and metrics such as word error rate.
5. Deployment: Finally, we will explore how to deploy our trained model for real-time lip reading applications.

Conclusion

Building a deep learning model for lip reading can be a challenging but rewarding task. With the right tools and techniques, it is possible to create a model that can accurately transcribe spoken words based on lip movements. By following this tutorial, you will gain a solid understanding of how to approach this problem and apply it to your own projects.

Happy coding!