This project aims to detect words by analyzing lip movements, leveraging computer vision and machine learning techniques. By using lip syncing technology, the system can accurately identify spoken words without relying on audio input.
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Lip Movement Analysis: The system analyzes lip movements in real-time or from recorded videos to identify words.
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Word Recognition: Using machine learning models, the system matches lip movements to a database of known words for accurate word detection.
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Scalability: The system is designed to scale for different languages and accents, making it versatile for various applications.
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Clone the repository:
git clone https://github.com/anishsoni29/LipNet cd LipNet -
Install dependencies:
pip install -r requirements.txt
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Download pre-trained models (if applicable) and place them in the designated folders.
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Start the application:
python main.py
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Follow the on-screen instructions to either provide a live video feed or a recorded video for lip syncing analysis.
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The system will display the detected words based on the lip movements.
Contributions are welcome! If you have any ideas for improvements or bug fixes, please open an issue or submit a pull request.
This project is licensed under the MIT License.
- This project was inspired by the advancements in computer vision and speech recognition technologies.
- Special thanks to [Nicholas Renotte] for their contributions to the lip syncing algorithms used in this project.