Guojian Xian

Papers

Total Citations

H-Index

About

Guojian Xian is a researcher at the forefront of applying deep learning to library automation and document analysis. His work centers on the development of intelligent systems for the segmentation and recognition of on-shelf books, a critical challenge in modern library management. Xian’s most notable contribution, the paper "Library on-shelf book segmentation and recognition based on deep visual features" (2022), has garnered 18 citations, establishing a foundational approach for using advanced visual features to accurately identify individual books in crowded shelf environments. This research directly addresses the practical need for efficient inventory tracking and automated retrieval, blending computer vision with real-world library operations. By leveraging deep neural networks, Xian’s method improves the precision of book spine detection and label reading, reducing manual labor and error rates. His work is particularly impactful for large-scale libraries and digital archives, where rapid, non-invasive book recognition is essential. Xian’s achievements mark him as a key contributor to the intersection of artificial intelligence and information science, offering scalable solutions that enhance accessibility and organization in knowledge repositories.