Sign Language Recognition Application for Deaf and Dumb People

Our goal is to develop a model that can detect and movements and signs. We'll train a simple gesture detecting model for sign language conversion, which will allow people to communicate with persons who are deaf and mentally challenged. This project can be performed using a variety of methods, including KNN, Logistic Regression, Nave Bayes Classification, Support Vector Machine, and CNN. The method we have chosen is CNN because it has a higher level of accuracy than other methods. A computer program written in the programming language Python is used for model training based on the CNN system. By comparing the input with a pre-existing dataset created using Indian sign language, the algorithm will be able to understand hand gestures. Users will be able to recognize the signs offered by converting Sign Language into text as an output. by a sign language interpreter This approach is implemented in Jupyter Lab, which is an add-on to the Anaconda documentation platform. To improve even more, we'll convert the inputs to black and white and accept input from the camera after applying the Background subtraction approach. Because the mask is configured to identify human skin, this model doesn't need a simple background to work and can be constructed with just a camera and a computer.