Reproducible Multimodal Artificial Intelligence Workflow for Historical Digital Archive Discovery.
Historical digital archives are increasingly searchable, but discovery remains limited when optical character recognition errors, visually heterogeneous document types, and sparse metadata are handled separately. This study aimed to develop a reproducible protocol to evaluate whether a conservative multimodal artificial intelligence workflow can improve archival retrieval without replacing archivist review. A corpus of 1,600 digitized archival records from eight document classes was assembled, and a 420-query benchmark was used to compare five retrieval conditions: baseline indexing, optical character recognition correction alone, visual classification alone, metadata enrichment alone, and full multimodal integration. Record-level outcomes included character error rate (CER), word error rate (WER), named-entity recall, document-type classification accuracy, prediction confidence, metadata completeness, and subject-heading match. Query-level outcomes included P@10, R@10, nDCG@10, time to first relevant result, and successful-search rate. Optical character recognition correction reduced WER most strongly for handwritten letters, ledgers, and registry books; visual fine-tuning improved classification accuracy most for maps, posters, newspapers, and registry books; and metadata enrichment increased completeness across all historical periods. The full multimodal condition achieved the highest retrieval performance (P@10 = 0.624, R@10 = 0.492, nDCG@10 = 0.634) and reduced the mean time to first relevant result from 156.079 s to 58.485 s. These results support a modular, auditable workflow in which text, image, and descriptive signals are combined under explicit thresholds and human review.