Sign Language Detection using CNN

Abstract

Hand signs are a viable type of human-to-human correspondence that has various potential applications. Being a characteristic method for collaboration, they are generally utilized for correspondence purposes by discourse impeded individuals around the world. As a matter of fact, around one percent of the Indian populace has a place with this class. This is the key motivation behind why it would meaningfully affect these people to integrate a structure that would figure out Indian Gesture based communication. In this paper, we present a method that utilizes the Pack of Visual Words model (BOVW) to perceive Indian communication via gestures letter sets (A-Z) and digits (0-9) in a live video transfer and result the anticipated marks as text as well as discourse. Division is done in view of skin tone as well as foundation deduction. In this paper we are going to use convolutional neural network for sign language detection. The convolutional neural network extracts the extraction and classification of sign category. We get 98.45% accuracy for 100 epochs.