Visual Language Interpreter

Meaningful communication is a basic human need, and yet there are some people who make use of sign language to communicate with the spoken word and encounter serious obstacles. This disconnect can leave us feeling isolated and alienated. Our project aims to solve this issue by creating a system which recognizes few hand signs that real-time converts into spoken as well written text. Our aim is to create a solution that can enable efficient natural language processing and an efficient gesture recognition, which will be based on convolutional Networks (CNN) and deep learning technology. Our text prediction: It improves the translation provided in terms of accuracy and relevance, as well as shortens processing time and communication. CNNs are a type of deep models, and designed to process structured data represented in form of 2D grids or multiarray like digital images. They operate by extracting and understanding features from visual inputs, using a hierarchy of filters that automatically recognize different patterns at increasing levels. Sign language is a critical example of the nuanced gestures these features would enable us to better understand. Our system then generates and can identify these different hand movements quite accurately. This enables these same gestures to be translated effortlessly into both speech and text, thus improving communication for sign language dependent persons. In addition, our solution consists of leading-edge text prediction technologies for optimization in translation. The purpose of these algorithms — increasing the accuracy and relevance of translations while at the same time decreasing both processing times, rendering communication quicker and more natural.