Gesture Art Board: Advancing Digital Expression Through Hand Gesture Recognition Technology

This research introduces an innovative Gesture Art Board system designed to transform digital whiteboard interactions in educational and professional environments. Traditional digital whiteboards require physical input devices such as styluses, mice, or direct touch interfaces, creating accessibility barriers and increasing implementation costs. Our proposed system addresses these limitations by implementing advanced computer vision and gesture recognition algorithms that enable users to control whiteboard functions through natural hand movements in three-dimensional space. The Gesture Art Board incorporates sophisticated features including gesture-based color selection, intuitive navigation controls, and erasure functions activated through simple hand wave motions. The system architecture employs optimized data structures and real-time processing algorithms to ensure smooth performance during complex gesture sequences. Comprehensive testing reveals the system achieves 91 % gesture recognition accuracy while maintaining 24 frames per second processing speed, eliminating the need for external hardware dependencies. Performance evaluations demonstrate the system's effectiveness across diverse applications, from educational instruction to digital art creation. The gesture recognition framework successfully processes rapid and intricate hand movements with minimal latency, addressing common limitations found in conventional whiteboard systems. Results indicate significant improvements in user engagement and accessibility compared to traditional input methods. This work contributes to the growing field of touchless human-computer interaction by demonstrating the viability of gesture-controlled educational technologies. The findings suggest substantial potential for expanding natural interaction paradigms in digital learning environments, offering enhanced accessibility and user experience without compromising functionality or precision. Future developments aim to expand gesture vocabulary and integrate additional interactive features for broader application domains.