Gesture Intelligence - Advancing Deaf-Mute Communication

For individuals classified as deaf or mute, hand gestures serve as their primary mode of communication, enabling them to convey their ideas, emotions, and thoughts through precise hand movements. But interacting with those who don't understand sign language is a problem. A hand gesture recognition software that can be utilized in hardware is created to close this gap. To achieve greater accuracy, this presentation compares two approaches - Convolutional Neural Networks (CNN) and a hybrid model - for hand gesture detection as input. To achieve optimal accuracy and show the results in an intuitive interface, preprocessing methods and feature extraction principles are employed.