Rabbit SignLink
Research and teaching project for sign-language recognition with computer vision, CNN/SIFT matching and voice feedback.
Rabbit SignLink is a compact research and teaching prototype for sign-language learning. The project covers data collection, gesture labeling, CNN/SIFT-based recognition, testing flows, PyQt desktop UX and a simple system architecture for turning recognized signs into text and speech feedback.
For Academic & Research Community: This project was originally designed as an open-source research initiative to explore accessibility solutions for the deaf and hard-of-hearing community. Archived Status: The project is officially archived and no longer actively developed; core dependencies such as legacy Keras/TensorFlow versions may be outdated. Archiving Purpose: All current updates and configurations are maintained solely for archival, historical preservation, and academic reference.
Project Highlights
Research and teaching project for sign-language recognition with computer vision, CNN/SIFT matching and voice feedback.
A useful sign-language learning prototype needs clear data preparation, repeatable model testing and responsive feedback while camera frames, recognition logic and audio playback run together.
The system combines OpenCV hand-region processing, CNN classification for alphabet signs, SIFT/FLANN matching for custom gestures, PyQt threading, local language processing and Edge TTS playback.
Built a research-oriented sign-language recognition prototype for learning and teaching use.
Video & Walkthrough
Timeline
Behind The Project
Overview
The project is intentionally scoped as an academic and instructional system: demonstrate the full loop from dataset preparation to inference, UI feedback and system testing.
Problem
A useful sign-language learning prototype needs clear data preparation, repeatable model testing and responsive feedback while camera frames, recognition logic and audio playback run together.
Approach
The system combines OpenCV hand-region processing, CNN classification for alphabet signs, SIFT/FLANN matching for custom gestures, PyQt threading, local language processing and Edge TTS playback.
Gallery
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