Year: 2025
Type: conference-paper
Abstract: Communication plays a vital role in conveying messages, expressing emotions, and sharing perceptions, becoming a fundamental aspect of human interaction with the environment. For individuals with hearing impairments, sign language serves as an essential communication tool, enabling interaction both within the deaf community and with non-deaf individuals. This study aims to bridge this misconception by developing an iconic language recognition system using the Deep Learning-based YOLO-v11 algorithm. YOLO-v11, a state-of-the-art object detection algorithm, is known for its speed, accuracy, and efficiency. The system uses image recognition to identify hand gestures in ASL and translates them into text or speech, facilitating inclusive communication. The accuracy of the training model is 94.67%, and the accuracy of the testing model is 93.02%, indicating that the model has excellent performance in recognizing sign language from the training and testing datasets. Additionally, the model is very reliable in recognizing the classes “Hello”, “I Love You”, “No”, and “Thank You” with a sensitivity close to or equal to 100%. This research contributes to advancing communication equality for individuals with hearing impairments, promoting inclusivity, and supporting their integration into society.
Authors: Ivana Lucia Kharisma, Irma Nurmalasari, Yuni Lestari, Salma Dela Septiani, Kamdan, Muchtar Ali Setyo Yudono
Institutions: Nusa Putra University, Sultan Ageng Tirtayasa University
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: gold
OpenAlex ID: W4414204169