No abstract

There is a communication lag between deaf-mutes and normal people. To overcomethat, we are providing information access and services to deaf-mute people in Indian Sign Language (ISL) and developing a flexible project that can be enlarged to capture the entire lexicon of Indian Sign Language via physical gestures like hand expressions and non-manual signs like facial expressions by developing and building a training model using machine learning algorithms. Sign language recognition uses image-based manual and non-manual gestures. Here we used figure recognition to identify manual and non-manual gestures. Finding expression gestures and analyzing finger movements to determine what the deaf-dumb individual is saying. In Python, the MediaPipe recognizes the hand signs and facial gestures of a person. These modules were developed to assist people with non-identical motions. This paper presents figure identification of Indian Sign Language via hand and facial gestures, as well asits integration with a chatbot as transcript output.