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Real-time hand gesture to speech pipeline using MediaPipe, finger counting, Morse encoding, text decoding, and desktop GUI interaction.
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A real-time Computer Vision prototype that converts hand gestures into Morse-based text and speech output through a desktop interface and live webcam runtime.
This project was developed as an academic Computer Vision prototype to explore gesture-based interaction, real-time hand tracking, and alternative communication pipelines.
Instead of aiming at full sign-language translation, it focuses on a simplified but technically explicit flow:
hand gesture -> finger count -> Morse-based encoding -> decoded text -> speech output
Camera -> MediaPipe Hands -> Finger Counting -> Morse Encoding -> Text Decoding -> Speech Output
The realtime runtime includes a few gesture rules in addition to basic finger counting:
250 ms5 acts as a space0 for about 2.5 seconds clears the current sequence and decoded sentence1 to 4 repeats the same digit, making consecutive equal inputs possible without lowering the hand each timesrc/gesture_morse_speech/: core application codeexperiments/: classic Computer Vision demosdocs/: architecture and technical notesmedia/: presentation slides and project mediamodels/: MediaPipe model assetrun_app.py: desktop GUI launcherrun_realtime.py: direct realtime runtime launchergesture-morse-speech/
|-- assets/
|-- docs/
|-- experiments/
|-- media/
| |-- slides/
| `-- videos/
|-- models/
|-- src/
| `-- gesture_morse_speech/
| |-- app/
| |-- audio/
| |-- config/
| |-- encoding/
| |-- gui/
| |-- utils/
| `-- vision/
|-- tests/
|-- README.md
|-- requirements.txt
|-- run_app.py
`-- run_realtime.py
python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt
Desktop application:
python run_app.py
Realtime pipeline directly:
python run_realtime.py
Unit tests:
python -m unittest tests.test_encoding
This repository is structured as an academic prototype and portfolio-ready codebase. The focus is on clarity of pipeline, modularity, and reproducibility of the current project behavior.
This project is a gesture-based academic prototype. It does not implement full sign-language recognition or natural-language translation.
For presentation material and visual assets, faces were anonymized using adaptive Gaussian blur proportional to face size. The configuration used was blur_ratio = 0.35, with a minimum kernel size of 45x45 and effective kernels ranging from 45x45 up to 159x159, depending on face dimensions.
Recommended locations for project materials:
media/videos/media/slides/