Abstract:

Sign language recognition systems play a crucial role in facilitating communication for individuals with hearing impairments. This research focuses on the implementation of a sign language recognition device using image processing techniques on a Raspberry Pi platform equipped with a camera module. The primary objective is to enhance the accuracy and robustness of the recognition system by augmenting the existing dataset with additional sign language gestures.

The methodology involves utilizing image processing algorithms, including image segmentation, feature extraction, and classification techniques such as machine learning models or deep learning networks. The Raspberry Pi serves as an embedded system capable of real-time processing, making it suitable for practical applications in diverse environments.

The contributions of this study include the development and optimization of algorithms tailored for low-resource devices like the Raspberry Pi, along with the expansion of the sign language dataset to encompass a broader range of gestures. Experimental results demonstrate the effectiveness of the proposed system in accurately recognizing sign language gestures, thereby improving communication accessibility for the hearing impaired.