Eye-Move: An Eye Gaze Typing Application with OpenCV and Dlib Library

People who are unable to type on a computer or a mobile phone due to inadequacies produced by their hands, such as osteoarthritis, carpal tunnel syndrome, trigger finger, Ganglion cysts, and other disorders, can benefit from eye vision technology. There are currently a number of commercial and non-commercial eye-tracking solutions available, including model-based and appearance-based methods; however, some of these solutions are expensive or unreliable in real-world situations, and others require explicit user calibration, which can be time-consuming. As a result, research into deep learning-based eye-tracking systems have switched to improving these systems. Recent eye-tracking research has focused on the development of deep learning-based eye-tracking algorithms that don't require explicit user calibration. Because of the recent emergence of deep learning, gaze estimation models based on convolutional neural networks (CNNs) are becoming more significant and common. In our research, the proposed system will provide a new user -friendly keyboard interface for the user. User can see the keyboard layout and can type the text by the movement of his or her eyes. The user can enhance his typing rate by making use of a word prediction engine. Word prediction is an assistive technology tool that suggests words while typing.