Recognition of Hand Movements for Specially Abled

The ability to recognize gestures allows a user to employ fluid gestures to deliver or transmit communication, information, facts, emotions, and sentiments. For those who do not grasp the essence of it and hence fail to comprehend it, sign language remains the most fundamental way of communicating or exchanging information to this day. These people are classified as specially-abled. In this study, a model is proposed in which the user makes a gesture in front of the camera, which is then processed through a filter and then a classifier to help the algorithm determine which gesture is made. Also given is indeed a greater knowledge of this model and the procedure. The CNN model was employed to achieve the goal, which gathers up images in real time and translates them into textual form. The text was shown on a graphical user interface that was created with Python's ‘tkinter’ GUI package. As a consequence of the research, a CNN model was created that accurately predicted proper output for images with an accuracy of 95.8% with only one layer and 98.0 percent with both layers.