  This paper presents a hand shape classification approach employing multiscale
template matching. The integration of background subtraction is utilized to
derive a binary image of the hand object, enabling the extraction of key
features such as centroid and bounding box. The methodology, while simple,
demonstrates effectiveness in basic hand shape classification tasks, laying the
foundation for potential applications in straightforward human-computer
interaction scenarios. Experimental results highlight the system's capability
in controlled environments.
