Convolutional neural network-based sign language translation system

Translation System (TS) is a system used by people withe disabilities to contact with other normal persons in the socoty. Since computers are an essential component of our life, the progress of human-computer interaction (HCI) has supported deaf and dumb people. And the important purpose of our suggest system is to progress an smart system which can turn as a translator between normal and deaf or dumb peopel and can be the communication path between people with speaking deficiency and normal people with both effective and efficient ways. The proposed system consists of a Convolutional Neural Network (CNN) based on the deep learning algorithm for effective extraction of handy properties to recognize the American Sign Language (ASL), for classifying the hand sign. This paper constructs to interpret ASL and also gives a complete overview of deep learning-based methodologies for gesture distignwishes. The proposed solution was tested on data samples from ASL data sets and get an overall accuracy of 96.68%. The proposed system was suitable and reliable for Deaf persons. Furthermore, an efficient and low-cost Hand Gesture Recognition (HGR) system for the real-time video stream from a mobile device camera. A separate individual hand gesture is utilized for validation in this article. The proposed system has to be designed with the front of the camera and the output is given in the form of text or audio.