Depth sensors allow us to acquire more data which improves accuracy and processing speed. Sign language reorganization bridges the gap between communication between deaf community and hearing majority. With the help of CNN from depth maps we have automate fingerspelling recognition system. CNN is more powerful than ever due to the recent achievements in GPU technologies and now it has been used to solve a plethora of computer vision problems. We have taken the help of deepness radar & convolution neural nexus to build an accurate sign language identification technique. Sign Language Recognition is a game-changer for deaf-mute persons, and it's been studied for years. Unfortunately, each study has its own set of restrictions and cannot be used commercially. Some studies have proven to be successful in identifying sign language, but commercialization is prohibitively expensive. Researchers are now paying greater emphasis to building commercially viable Sign Language Recognition systems

Sign language, RUB, gestures, Image Reorganization, Open CV, Image Processing, Python, Machine Learning