A Novel Survey on Hand Sign Recognition

Motion recognition is an essential research part of computer vision with many applications including human-computer interaction and signal interpretation. In this article, we propose a convolutional neural network (CNN) model for motion recognition using live cameras. We gathered information about hand-drawn illustrations of the English alphabet (A-Z) before removing the background and converting them to black-and-white images. Previous images were used to train the CNN model and 95% accuracy was achieved on the test data. We implemented a model in Python and combined it with a user-friendly software interface that provides a real-time prediction of hand movements using a camera system. Our system has the potential to be an essential tool in many industries and businesses, including managing electronic devices and helping people with communication disabilities. The proposed models and methods provide robust and accurate solutions for real-time operational information and can be used in a variety of situations.