AI-Driven Cultural Artifact Recognition

Cultural heritage preservation represents one of the most significant challenges in maintaining humanity’s historical legacy for future generations. Traditional methods of artifact identification, cataloging, and preservation have relied heavily on manual processes and expert knowledge systems, resulting in scalability constraints and accessibility limitations. This comprehensive survey examines the transformative role of artificial intelligence and deep learning technologies in revolutionizing cultural heritage preservation, with particular emphasis on automated artifact recognition systems. Through systematic analysis of recent technological advances, this paper presents a thorough investigation of Convolutional Neural Networks (CNNs), computer vision techniques, and AI-enhanced content generation applications in cultural heritage domains. The survey synthesizes findings from multiple research initiatives demonstrating that deep learning approaches can achieve remarkable accuracy rates, with CNN-based systems achieving up to 91% accuracy in cultural artifact classification tasks. This paper provides critical insights into the integration of modern AI technologies with traditional preservation methods, highlighting both technological capabilities and implementation challenges. The findings indicate significant potential for AI-driven systems to democratize access to cultural heritage knowledge while maintaining educational value and historical accuracy.