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Volume 107
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Proceeding Paper

AI-Powered Sign Language Detection Using YOLO-v11 for Communication Equality †

by
Ivana Lucia Kharisma
1,*,
Irma Nurmalasari
1,
Yuni Lestari
1,
Salma Dela Septiani
1,
Kamdan
1 and
Muchtar Ali Setyo Yudono
2
1
Informatics Engineering Department, Nusa Putra University, Sukabumi 43152, Indonesia
2
Electrical Engineering Department, Sultan Ageng Tirtayasa University, Serang 42182, Indonesia
*
Author to whom correspondence should be addressed.
†
Presented at the 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society, Aizuwakamatsu City, Japan, 20–26 January 2025.
Eng. Proc. 2025, 107(1), 83; https://doi.org/10.3390/engproc2025107083
Published: 8 September 2025
(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)
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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.
Keywords: sign language; YOLO; communication; equality sign language; YOLO; communication; equality

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MDPI and ACS Style

Kharisma, I.L.; Nurmalasari, I.; Lestari, Y.; Septiani, S.D.; Kamdan; Yudono, M.A.S. AI-Powered Sign Language Detection Using YOLO-v11 for Communication Equality. Eng. Proc. 2025, 107, 83. https://doi.org/10.3390/engproc2025107083

AMA Style

Kharisma IL, Nurmalasari I, Lestari Y, Septiani SD, Kamdan, Yudono MAS. AI-Powered Sign Language Detection Using YOLO-v11 for Communication Equality. Engineering Proceedings. 2025; 107(1):83. https://doi.org/10.3390/engproc2025107083

Chicago/Turabian Style

Kharisma, Ivana Lucia, Irma Nurmalasari, Yuni Lestari, Salma Dela Septiani, Kamdan, and Muchtar Ali Setyo Yudono. 2025. "AI-Powered Sign Language Detection Using YOLO-v11 for Communication Equality" Engineering Proceedings 107, no. 1: 83. https://doi.org/10.3390/engproc2025107083

APA Style

Kharisma, I. L., Nurmalasari, I., Lestari, Y., Septiani, S. D., Kamdan, & Yudono, M. A. S. (2025). AI-Powered Sign Language Detection Using YOLO-v11 for Communication Equality. Engineering Proceedings, 107(1), 83. https://doi.org/10.3390/engproc2025107083

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