Treffer: Greek Sign Language Detection with Artificial Intelligence.

Sign language serves as a vital way to communicate with individuals with hearing loss, deafness, or a speech disorder, yet accessibility remains limited, requiring technological advances to bridge the gap. This study presents the first real-time Greek Sign Language recognition system utilizing deep learning and embedded computers. The recognition system is implemented using You Only Look Once (YOLO11X-seg), an advanced object detection model, which is embedded in a Python-based framework. The model is trained to recognize Greek Sign Language letters and an expandable set of specific words, i.e., the model is capable of distinguishing between static hand shapes (letters) and dynamic gestures (words). The most important advantage of the proposed system is its mobility and scalable processing power. The data are recorded using a mobile IP camera (based on Raspberry Pi 4) via a Motion-Joint Photographic Experts Group (MJPEG) Stream. The image is transmitted over a private ZeroTier network to a remote powerful computer capable of quickly processing large sign language models, employing Moonlight streaming technology. Smaller models can run on an embedded computer. The experimental evaluation shows excellent 99.07% recognition accuracy, while real-time operation is supported, with the image frames processed in 42.7 ms (23.4 frames/s), offering remote accessibility without requiring a direct connection to the processing unit. [ABSTRACT FROM AUTHOR]

Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)