CNN Based Real Time Recognition of Hand Gestures for Indian Sign Language

Human Centered Computing is a rising field of research having goal of understanding behavior of human being. In this field, Sign Language Recognition is noteworthy application with many challenges. It bridges the communication gap that separates hearing individuals from deaf people. In this article, a computer vision techniques based application for recognising Indian Sign Language (ISL) is presented. Convolutional Neural Networks (CNNs), the most remarkable kind of algorithms for deep learning, are used in its construction. Initially, the self built dataset containing training images has been used to train the system. The system interprets a variety of live camera gestures into text and voice. To further enhance the system's usability, a graphical user interface (GUI) is also built. Additionally, it also offers rotation, translation, and scale invariance. The CNN's weight sharing and sparse connectivity make the suggested system incredibly effective in the context of storage and performance. It is found to be an extremely accurate system with a 99.44% recognition rate during the testing.