UC Berkeley AI Research Initiative

Centuries-old maritime records transformed into searchable academic datasets 70% faster through AI-led transcription. A leading global research university required a scalable archival intelligence model to extract migration data from fragile handwritten maritime registries and historical manuscripts. Traditional academic transcription workflows were slow, difficult to scale and unable to consistently interpret degraded scripts, faded ink and centuries-old cursive writing with the precision required for peer-reviewed research.

"AI-powered archival transcription transformed handwritten maritime records into searchable academic datasets, accelerating historical research and improving large-scale scholarly accessibility. "

Industry: Education
Service: Generative AI, Intelligent Document Processing

70%: Faster processing vs manual transcription
AI-driven: Extraction of complex historical handwriting
Global: Research-ready academic datasets delivered

Challenge

The archive contained fragile maritime registries filled with complex historical handwriting styles, faded annotations and inconsistent formatting that standard OCR systems could not interpret accurately. Manual academic transcription required extensive time and specialist expertise, while research outputs demanded citation-grade precision and full auditability across every extracted dataset.

Approach

SBL Infotech deployed ACTIGEN alongside DAMS — its Digital Archives Management System — to govern the complete archival extraction workflow from digitisation through cognitive transcription, validation and structured data delivery. High-resolution imaging workflows captured fragile manuscripts while AI-assisted interpretation models processed difficult cursive scripts and degraded historical text. Senior linguistic specialists and academic validation teams reviewed extracted records through multi-level quality control frameworks to ensure historical accuracy and citation readiness. Structured metadata pipelines transformed previously inaccessible archival records into searchable datasets aligned to migration research and historical analysis initiatives.

Outcome

The university transformed fragmented archival research processes into a scalable historical intelligence platform capable of accelerating thesis development, migration analysis and cross-border academic collaboration. Researchers gained rapid access to structured, searchable historical datasets that previously required months of manual interpretation. The engagement established a repeatable cognitive archival model capable of unlocking complex handwritten records for universities, libraries and research institutions globally — preserving historical narratives while enabling faster, data-driven academic discovery at institutional scale.

IX Case studies

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