LipCap is an advanced AI-powered lip reading system designed to interpret silent video footage and automatically generate accurate text captions based on the speaker’s lip movements. It combines the power of computer vision, deep learning, and natural language processing (NLP) to bridge the gap between visual cues and spoken language.
The main objective of LipCap is to enable speech understanding without audio, making communication possible in:
Noisy environments where microphones fail
Silent communication systems (e.g., military or surveillance)
Accessibility tools for individuals with hearing or speech impairments
Future human–computer interaction systems
LipCap processes a silent video input and predicts the words being spoken purely by analyzing lip movements. It uses CNNs to extract frame-level spatial features and RNNs/LSTM layers to learn temporal patterns between frames, allowing it to “read” the lips with remarkable accuracy.
With its simple Flask-based web interface, users can upload a video and instantly get an AI-generated caption. The model can also be retrained or fine-tuned for new datasets, accents, or languages — making LipCap a flexible foundation for advanced visual speech recognition research.
In short, LipCap brings artificial intelligence closer to understanding human communication through vision alone — a step toward the future of silent speech recognition.
🎥 Video-to-Text Conversion: Automatically converts lip movements into text captions.
🤖 Deep Learning Model: Uses CNN and RNN layers for spatial and temporal feature extraction.
🧩 Pre-Trained Model Integration: Includes trained weights for accurate inference.
🌐 Web Interface (Flask): Upload and test videos directly from your browser.
⚡ Near Real-Time Prediction: Supports short clips for instant results.
🧠 Custom Training: Can be extended to new datasets or languages.
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Programming Language: Python
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Deep Learning Frameworks: TensorFlow / Keras / PyTorch
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Web Framework: Flask
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Frontend: HTML, CSS, JavaScript
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Utilities: NumPy, OpenCV, h5py, ffmpeg
Upload a short silent video clip of a person speaking.
Wait a few seconds while the model processes the video.
The generated caption will appear on the screen.