Sign Language Detection Using Open Cv

• Published in: 2024 International Conference on Communication, Computing and Internet of Things (IC3IoT) pp. 1 - 5
• Main Authors: Shabana Parveen, M, Keerthana, R G, Shanmathi, S, Shajitha, M
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.04.2024
• Subjects: CNN; Computer vision; Convolutional neural networks; HOG; LCD; Opencv; Raspberry Pi 4; Real-time systems; Sign language; Speech recognition; Telecommunication computing; User experience
• DOI: 10.1109/IC3IoT60841.2024.10550232

We frequently encounter people who suffer from disabilities like blindness and deafness. Communication difficulties arise when deaf-mute people try to communicate with non-sign language-using people. A robust automatic sign language recognition system that promotes accessibility and inclusivity within this community may be a viable solution to this issue. The goal of this proposal is to develop a device that can convert a deaf person's hand gestures into text and voice. The system records video from the camera using an open-source computer vision toolkit called OpenCV. It then processes the video using a variety of image processing techniques, including convolutional neural networks (CNN) and Histogram of Gradient (HOG). After that, a Raspberry Pi 4 analyzes the motions that were recorded, compares the results with a database, and displays the results on an LCD and speaker. Through an easy interface, users can customize the output mode to their favorite sign language dialect and choose between text and speech, improving the entire user experience. Comprehensive evaluation and testing have shown that real-time performance and accuracy in gesture detection are promising. We think that the inclusion and communication of deaf-mute people could be greatly enhanced by our OpenCV-based sign language detection system.