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This paper presents a user interface designed to enable computer cursorcontrol through hand detection and gesture classification. A comprehensive handdataset comprising 6720 image samples was collected, encompassing four distinctclasses: fist, palm, pointing to the left, and pointing to the right. Theimages were captured from 15 individuals in various settings, including simplebackgrounds with different perspectives and lighting conditions. Aconvolutional neural network (CNN) was trained on this dataset to accuratelypredict labels for each captured image and measure their similarity. The systemincorporates defined commands for cursor movement, left-click, and right-clickactions. Experimental results indicate that the proposed algorithm achieves aremarkable accuracy of 91.88% and demonstrates its potential applicabilityacross diverse backgrounds.

Control of Computer Pointer Using Hand Gesture Recognition in Motion Pictures