What question did this study set out to answer?

The research aims to develop a real-time system for translating sign language to facilitate better communication between signers and non-signers.

January 17, 2026Open Access

AI Sign Language Translation System

Key Points

The research aims to develop a real-time system for translating sign language to facilitate better communication between signers and non-signers.
Integrated a convolutional neural network for image-based gesture classification.
Utilized a MediaPipe-based multilayer perceptron for hand landmark feature analysis.
Deployed the system as a web application using Flask for real-time video streaming.
Conducted experiments to compare the accuracy and efficiency of CNN and MLP models.
CNN model achieved higher accuracy for classifying complex gestures.
MLP model demonstrated faster inference times and better computational efficiency.
The system's modular design allows for easy expansion to include additional sign language vocabularies.

Abstract

This preprint presents a real-time sign language translation system designed to bridge communication gaps between sign language users and non-signers. The system integrates two complementary deep learning approaches: a convolutional neural network (CNN) for image-based gesture classification and a MediaPipe-based multilayer perceptron (MLP) using hand landmark features. The application is deployed as a web-based platform using Flask, enabling real-time video streaming and live gesture recognition via standard webcams. Experimental results demonstrate that the CNN model achieves higher accuracy for complex gestures, while the MLP model provides faster inference and improved computational efficiency. The modular and extensible design allows easy expansion to additional sign vocabularies and supports future research in assistive and accessibility-focused technologies.

AI Sign Language Translation System

Key Points

Abstract

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