SMART METADATA MANAGEMENT FOR PRINT ARCHIVES - Chandwaskar, Rohit; Kannan, Sathyabalaji; Sahoo, Srikanta Kumar; Manhas, Rashmi; Singh, Takveer; barla, Ms. Usha Kiran (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Metacognitive metadata management of print archives is one of the essential steps that must be taken to integrate the old archival methodology with the new digital intelligence. With the shift of the cultural institution and libraries towards dynamic digital environments, replacing the static cataloging systems with the dynamic digital ecosystems, the needs of making the metadata efficient, accurate, and information-rich increases manifold. This paper will describe an intelligent model of metadata management, which uses Artificial Intelligence (AI), Optical Character Recognition (OCR), Natural Language Processing (NLP), and linked data frameworks to perform intelligent work on archives. The framework suggested combines automated metadata extraction, real-time validation, semantic enrichment, and entity recognition based on machine learning pipelines that are compatible with existing metadata standards (dublin core, MARC, METS and PREMIS). A prototype implementation presents the ways AI-based workflows can be applied to facilitate metadata completeness, interoperability, and retrieval accuracy in digitized print repositories. Scanned archival data has been evaluated through experimental evaluations where there is a significant difference in accuracy and efficiency compared to traditional manual cataloging systems. Performance insights, system architecture, scalability and semantic alignment issues and long-term preservation implications are also addressed in the study. Finally, the study will add a model of the future that unites automation with semantic intelligence, improves discoverability, sustainability, and accessibility of cultural heritage collections with smart metadata governance and intelligent interoperability of archival.