  This paper presents a user interface designed to enable computer cursor
control through hand detection and gesture classification. A comprehensive hand
dataset comprising 6720 image samples was collected, encompassing four distinct
classes: fist, palm, pointing to the left, and pointing to the right. The
images were captured from 15 individuals in various settings, including simple
backgrounds with different perspectives and lighting conditions. A
convolutional neural network (CNN) was trained on this dataset to accurately
predict labels for each captured image and measure their similarity. The system
incorporates defined commands for cursor movement, left-click, and right-click
actions. Experimental results indicate that the proposed algorithm achieves a
remarkable accuracy of 91.88% and demonstrates its potential applicability
across diverse backgrounds.
