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Sign language has long been a fundamental mode of communication for deaf and mute individuals, serving as a crucial tool for inclusivity and interaction. Nonetheless, communication barriers persist as many individuals outside of these communities struggle to comprehend and utilize sign language effectively. A ground-breaking system known as hand gesture recognition using image processing to audio conversion has emerged to address this issue. This innovative system aims to develop an application capable of translating sign language and hand gestures into text and audio, thereby facilitating communication between deaf and mute individuals and the wider society. This system enables the detection and classification of hand gestures by employing computer vision techniques, such as CNN algorithms for image processing and the MediaPipe framework for hand gesture identification in real-time video streams. Subsequently, the system utilizes audio signals to provide immediate feedback by converting the detected gestures into corresponding sounds. The feature extraction CNN algorithm is implemented in Python, while the execution takes place on a Raspberry Pi connected to an external camera utilizing OpenCV libraries. Through this comprehensive approach, the system endeavours to bridge the communication gap and enhance the inclusion of deaf and mute individuals in various social settings.
Subashini et al. (Thu,) studied this question.