Vision based Interpreter for Sign Languages and Static Gesture Control using Convolutional Neural Network

Sign languages are the primary mode of communication for deaf people, and this interpreter aims to provide an effective means of communication between deaf and non-deaf individuals. The proposed system uses computer vision techniques to capture and interpret hand gestures and movements, and OpenCV is used as the primary tool for image processing and analysis. The system's accuracy and efficiency are evaluated using various metrics, and the results demonstrate its effectiveness in real-world scenarios The system aims to assist people with hearing and speech impairments by providing a means of communication through hand gestures and sign languages. The proposed system comprises of a camera module to capture the hand gestures and sign languages, a CNN-based image processing module for gesture recognition, and an OpenCV-based gesture control module for controlling external devices. The CNN-based image processing module is trained on a dataset of hand gestures and sign languages, to recognize and interpret the gestures accurately. The proposed system is evaluated on a set of benchmark datasets and achieved high accuracy in gesture recognition and control. The results of the study suggest that the proposed vision-based interpreter for sign languages and static gesture control can be an effective means of communication for people with hearing and speech impairments. The system recognized static hand postures and hand movements with a 94% accuracy rate. The system achieved an accuracy of 92% in recognizing the sign language gestures. The system's accuracy and robustness make it suitable for real-time applications, and it has the potential to improve the quality of life for people with disabilities.