Malak, PiotrLetowska, AgnieszkaWodzinski, JanCampana, StefanoFerdani, DanieleGraf, HolgerGuidi, GabrieleHegarty, ZackaryPescarin, SofiaRemondino, Fabio2025-09-052025-09-052025978-3-03868-277-6https://doi.org/10.2312/dh.20253133https://diglib.eg.org/handle/10.2312/dh20253133We present a multilayered methodology for processing digitized historical texts, enabling cross-relational analysis across time periods, languages, and subject domains. Drawing from multiple DH platforms (Tsadikim, Two Enlightenments, Corporeality), we demonstrate an integrated pipeline combining adaptive OCR, noise-tolerant keyword extraction, and NER. Custom preprocessing and fuzzy matching techniques allow for meaningful text recovery from degraded scans in Polish, German, and Yiddish. Data are enriched with spatial and temporal metadata, indexed by topic and linked across projects. The resulting datasets support trend analysis, social network modeling, and discourse mapping. Our approach enables researchers to trace linguistic shifts and intellectual networks over centuries without manual review of source pages. This workflow facilitates interoperable exploration of cultural data and demonstrates how machine learning can assist in recovering semantic relationships from fragmented historical records. The methodology was tested on Enlightenment-era and early 20th-century journals, revealing both technical challenges and insights into evolving ideological, medical, and theological vocabularies.Attribution 4.0 International LicenseCCS Concepts: Information systems → Digital libraries and archives; Computing methodologies → Natural language processing; Machine learning; Applied computing → Arts and humanities; Digital humanities; Human-centered computing → Visualization; Theory of computation → OntologiesInformation systems → Digital libraries and archivesComputing methodologies → Natural language processingMachine learningApplied computing → Arts and humanitiesDigital humanitiesHuman centered computing → VisualizationTheory of computation → Ontologies10.2312/dh.202531339 pages