Real-Time Sign Language Recognition System: Computer Vision Empowering Barrier-Free Communication

This project aims to build a real-time sign language gesture recognition system using computer vision technology to break the communication barrier between the hearing-impaired and hearing people. Based on MediaPipe, OpenCV, and Random Forest Classifier, the system realizes instant detection and prediction of five commonly used gestures (greeting, affirmation, negation, thanks, help), supports real-time interaction, and provides a technical solution for barrier-free communication.

Sign language is the main communication method for the hearing-impaired, but the gap between sign language and spoken language creates communication barriers. According to statistics, there are tens of millions of hearing-impaired people worldwide, who often face communication difficulties in daily life scenarios such as education, employment, and medical care. The emergence of real-time sign language recognition technology provides a technical possibility to break this barrier, which can convert sign language gestures into text or voice, and promote barrier-free communication between the hearing-impaired and society.

Technical Architecture Overview

This project adopts a classic computer vision pipeline combined with modern machine learning technology to realize end-to-end sign language recognition. The system architecture includes three core components: hand key point detection, feature extraction and representation, and gesture classification prediction.

Core Technical Details

• MediaPipe Hand Tracking: Uses Google's MediaPipe framework to detect 21 key point coordinates of the hand, which is robust to light changes and background complexity, and has high computational efficiency.
• OpenCV Video Processing: Responsible for video stream capture and preprocessing, providing standardized input to ensure smooth video processing.
• Feature Engineering: Designs geometric features such as relative positions of key points, finger bending angles, and palm orientation to improve the generalization ability of the model.
• Random Forest Classifier: Meets the needs of real-time performance and interpretability, and shows good accuracy and stability in classifying five gestures.

Supported Gesture Categories

Currently supports five commonly used basic gestures: greeting (hello), affirmation (yes), negation (no), thanks (thanks), help (help), covering core daily interaction scenarios.

System Performance Optimization

• Real-time Guarantee: Through MediaPipe lightweight model, OpenCV hardware acceleration, and Random Forest fast inference, it achieves a processing speed of 30 frames per second on ordinary devices.
• Stability Improvement: Introduces a temporal smoothing strategy to filter short-term noise and misrecognition, and outputs stable and reliable results.

Auxiliary Communication Tool

As a mobile phone or computer application, it converts the recognition results into text or voice to realize instant two-way communication between the hearing-impaired and hearing people.

Educational Assistance

Provides instant feedback in sign language teaching, points out deficiencies by comparing with standard gestures, and accelerates learners' skill mastery.

Public Service Windows

Deployed in hospitals, banks, government service halls and other places to provide convenient communication channels for the hearing-impaired and improve the inclusiveness of public services.

Vocabulary Expansion

Need to collect larger-scale datasets, introduce stronger deep learning models, and expand to a complete sign language vocabulary.

Continuous Sign Language Recognition

Solve problems such as gesture segmentation and temporal modeling, and expand from isolated gesture recognition to continuous sign language recognition.

Individual Difference Adaptation

Through online learning or transfer learning technology, the model can adapt to the gesture style of specific users and improve practicality.