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A touchless web canvas utilizing MediaPipe and TensorFlow.js for real-time 21-point hand landmark detection, enabling users to draw, erase, switch palettes, and manipulate shapes in mid-air through webcams.
A touchless web canvas utilizing MediaPipe and TensorFlow.js for real-time 21-point hand landmark detection, enabling users to draw, erase, switch palettes, and manipulate shapes in mid-air through webcams.
Interact with the live production deployment directly below across Desktop, Tablet, and Mobile viewports.
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Real performance data and business outcomes achieved for Proprietary Computer Vision Project.
Smooth real-time gesture tracking in standard browsers
Ultra-low client-side hand landmark tracking delay
No camera video ever leaves the user’s device
Running heavy machine learning hand tracking in client browsers without lag, frame drops, or requiring external software installations.
Engineered WebGL-accelerated canvas pipelines and optimized MediaPipe hand landmark tracking running at a silky smooth 60 FPS directly in the browser.
Key features and engineering highlights implemented
React integration & optimization
TypeScript integration & optimization
HTML5 Canvas API integration & optimization
Modern frameworks and tools selected for this architecture
I help businesses, startups, and founders build high-converting websites, web apps, and digital platforms.
Tailwind CSS integration & optimization
MediaPipe Hands ML integration & optimization
TensorFlow.js WebGL backend integration & optimization
Vite integration & optimization
Vercel integration & optimization
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