Next-Generation Digital Libraries: Integrating Emerging Technologies for Intelligent, Sustainable, and Accessible Knowledge Preservation
Keywords:
Digital libraries, Artificial intelligence in libraries, Optical character recognition, Knowledge graphsAbstract
Digital libraries are moving to be more dynamic knowledge networks and not static collections of digital resources. This paper presents a next-gen digital library design, one that incorporates new technologies Artificial Intelligence (AI), generative restoration, hybrid OCR-large language model (LLM) extraction, knowledge graphs, blockchain-based provenance, edge computing and accessibility API, to facilitate intelligent, sustainable and ethically controlled knowledge preservation. An architecture based on a modular, production-centric design is created with seven layers that include edge capture and IoT-enabled quality control, diffusion-based repairing, layout-aware OCR with retrieval-augmented LLM repairing, knowledge graph creation with authority reconciliation, blockchain based provenance management, OAIS compliant repository preservation and intelligent access layer in order to support semantic search and multimodal accessibility. The model is tested with the help of publicly available historical data and artificial degraded datasets. Character error rate (CER), metadata precision/recall, knowledge graph connecting accuracy, processing throughput, energy estimates and user-centered usability metrics are all performance metrics. The experimental evidence proves that restoration together with OCR-LLM correction achieves lower rates of character errors by up to 35-50% in damaged documents than OCR baselines. Metadata extraction with automated systems had a maximum accuracy of 91% on clean scans and integration of knowledge graphs enhanced the effectiveness of discovery. Edge-first computing minimized the cloud computation requirements and enhanced operation efficiency. Significant improvements in perceived legibility of restored documents, in average time to complete manual corrections, and in trust in the system were found when viewing the restored documents with the transparent provenance visualization interface, compared to when using a structured SUS questionnaire with a panel of librarians, digital humanities researchers and information science professionals in a simulated pilot evaluation. This work is a contrast to fragmented historical solutions: it is a proposed solution in the form of an unparalleled, auditable and sustainability-conscious digitization pipeline, balancing between automation and governance and human control. The proposed assessment system, architectural model and provenance-based design offer viable advice to agglomerate and institutional libraries in search of scalable, ethical and technology-enhanced digital preservation.
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