Valentinetemi

Valentinetemi / asl-sign-detection-mediapipe-rf

Public

Real-time ASL sign language detector using MediaPipe hand landmarks + XGBoost — 98.43% accuracy, skin-tone agnostic, no CNN

computer-science computer-vision data-science machine-learning python
13
4
100% credibility
Found Mar 23, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

A webcam-based tool that recognizes American Sign Language alphabet letters in real-time by watching hand shapes.

How It Works

1
🔍 Discover the sign tool

You hear about a handy tool that recognizes sign language letters using your computer's camera right away.

2
📥 Get the ready files

You grab the simple files from the sharing page to your computer, like downloading a fun app.

3
💻 Open the viewer

You launch the viewer program, and it wakes up your camera to watch your hands.

4
👋 Sign a letter

You hold up your hand to make an 'A' or any letter, and it spots the shape perfectly every time.

5
✨ See instant results

The screen lights up with the letter name, feeling magical as it gets most signs right on the first try.

🎉 Sign freely

Now you can practice or chat using signs, with the tool helping translate to words smoothly.

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Star Growth

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AI-Generated Review

What is asl-sign-detection-mediapipe-rf?

This Python project runs real-time ASL alphabet recognition straight from your webcam, spotting letters A-Z plus space and delete at 98.43% accuracy. Built with MediaPipe hand tracking and XGBoost classification, it delivers a skin-tone agnostic real-time ASL interpreter that sidesteps slow CNNs. You get instant webcam inference for fingerspelling, perfect as a proxy for languages like Nigerian Sign Language.

Why is it gaining traction?

It crushes alternatives with lightweight real-time detection—no raw images needed, just robust hand landmarks for consistent performance across skin tones and lighting. The 98.43% accuracy on 87,000 images hooks devs chasing real-time ASL recognition without GPU hassles. Plus, its normalization makes it scale-invariant, ideal for real-time ASL translation prototypes.

Who should use this?

Accessibility devs building real-time ASL translators or dashboards. Indie hackers prototyping webcam-based sign interpreters for deaf community tools. Researchers adapting it for NSL or other real-time gesture projects needing quick, high-accuracy starts.

Verdict

Solid proof-of-concept for real-time ASL with excellent docs and a trained model ready to run, but 13 stars and 1.0% credibility signal early maturity—prototype with it, don't ship yet. Worth forking if accessibility is your jam.

(178 words)

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