Dazhou Feng¹ and Tyler Boulom², ¹USA, ²California State Polytechnic University, USA

Visitors often struggle to fully understand and appreciate museum artworks due to limited contextual information. This project proposes AR glasses that scan artworks and display real-time, AI-generated descriptions, providing historical background, artistic techniques, and symbolic meanings [1]. The system integrates three core components: image capture, AI-based classification and description, and real-time on-screen display. Key challenges addressed include accurate recognition under variable conditions, personalized content delivery, and accessibility for diverse users. Experiments measured recognition accuracy and system latency, identifying areas for improvement such as low-light performance and network optimization. Comparisons with prior methodologies show advancements in portability, adaptability, and real-time personalization [2]. The results indicate that the system significantly enhances engagement, understanding, and accessibility in museum learning environments. By merging augmented reality with AI-driven context delivery, this solution has the potential to transform art appreciation, offering visitors an immersive, adaptive, and more meaningful connection to cultural heritage.

Augmented Reality (AR), Artificial Intelligence (AI), Museum Learning, Artwork Recognition