Sign Language Translator via Smartphone Image Analysis using Convolutional Neural Network
Year: 2024
Type: article
Abstract: This study presents the design and implementation of a smartphone-based image processing system aimed at translating sign language gestures into text in real-time. The primary objective was to create a platform that enhances communication between deaf individuals and non-sign language users through the accurate recognition of sign language gestures. Using Kotlin programming and MediaPipe, the system captures and processes gestures, converting them into readable text. The results demonstrated that the system is capable of recognizing and translating various sign language gestures with a high degree of accuracy and efficiency. The successful deployment of this system highlights its potential to break down communication barriers and offers a foundation for future improvements, including expanded sign language support and performance optimization for real-world applications.
DOI: 10.70979/lszo7444
Authors: John Carlo Torres, Roselie Alday
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405476346