GesturePro — Real-Time Sign Language Translation
Breaking the Communication Barrier
Skill Constellation
Primary
Supporting
Emerging
GesturePro is an interactive sign language translator that empowers hearing-impaired and aphonic individuals by using advanced AI to instantly translate sign language gestures into real-time text and speech. The platform uses computer vision and deep learning to recognize hand gestures through a webcam feed and convert them into readable text — bridging the communication gap in real time.
Hearing-impaired individuals face daily communication barriers. Existing translation tools are either expensive, non-real-time, or require specialized hardware.
A browser-based, real-time sign language translator using just a webcam — no special hardware needed. Powered by ML hand-tracking and gesture recognition.
Full-Stack AI Architecture
GesturePro is built as a three-tier architecture — a Next.js frontend for real-time video capture, a FastAPI backend for authentication and data management, and an ML pipeline for gesture recognition. The entire system is containerized with Docker for consistent deployment.
Three-Tier System Design
Architected full-stack system separating real-time frontend, API backend, and ML inference pipeline into independently deployable services.
Evidence: Docker Compose orchestration with health checks across 3 services.
Frontend
Next.js + TailwindCSS
Real-time webcam capture, video streaming UI, authentication flow (sign-in/sign-up), and responsive gesture translation display.
Backend
FastAPI + PostgreSQL
RESTful API with user authentication, session management, translation history storage, and health-checked Docker services.
ML Pipeline
Python + TensorFlow
Hand landmark detection via MediaPipe, processed training data, Jupyter notebooks for experimentation, and saved models for inference.
Computer Vision Pipeline (MediaPipe)
Implemented MediaPipe hand landmark detection extracting 21 key points per frame, creating skeletal hand representations for ML classification.
Evidence: Real-time 21-landmark tracking powering gesture recognition at webcam speed.
Project Structure
Languages
Codebase
Deployment
What GesturePro Does
The platform combines real-time computer vision with a clean, accessible interface to create a seamless translation experience.
ML Model Training (TensorFlow)
Trained TensorFlow classifier on MediaPipe landmark data to recognize ASL gestures with real-time inference capability.
Evidence: Working real-time gesture → text translation pipeline.
Real-Time Video Capture
Browser-based webcam access streams hand gestures directly to the ML model — no downloads or special hardware required.
Hand Landmark Detection
MediaPipe extracts 21 hand landmarks per frame, creating a skeleton representation of hand position and finger orientation.
AI Gesture Classification
TensorFlow model classifies hand landmarks into sign language letters and words with real-time inference.
Instant Text Translation
Recognized gestures are immediately converted to on-screen text, enabling fluid conversation without delays.
User Authentication
Secure sign-in/sign-up flow with session management, enabling personalized translation history and preferences.
Containerized Deployment
Docker Compose orchestrates all services (frontend, backend, database) with health checks for reliable deployment.
Accessibility Through Technology
GesturePro represents a step toward making communication universally accessible. By combining browser-based computer vision with deep learning, the platform removes the cost and hardware barriers that have historically limited sign language translation tools.
Gestures are recognized and displayed as text within milliseconds of capture.
Works with any standard webcam — no specialized sensors or gloves needed.
Containerized 3-tier system with auth, persistence, and ML inference.
Fully open-source on GitHub for community contribution and extension.
Accessibility-First Engineering
Built an accessible-by-default platform — zero cost, zero hardware, browser-only deployment democratizing ASL translation.
Evidence: Open-source, zero-hardware-cost solution deployed on Vercel.
Future Roadmap
Multi-Language ASL Support
Expand beyond ASL to include BSL, ISL, and other sign language systems for global accessibility.
Text-to-Speech Output
Add voice synthesis so translated text can be spoken aloud for two-way communication.
Mobile-First PWA
Progressive Web App for on-the-go translation using smartphone cameras.
Learning Analytics
Track user progress in learning sign language with personalized practice recommendations.