Enriching Historical Records: An OCR and AI-Driven Approach for Database Integration

TL;DR

This paper presents an automated pipeline combining OCR, AI interpretation, and database linking to digitize and integrate historical records from Leiden University, achieving high accuracy in data extraction and record linkage.

Contribution

It introduces a novel AI-driven method for harmonizing historical document data with existing databases, improving automation and accuracy in digital humanities research.

Findings

OCR achieved 1.08% CER and 5.06% WER.

JSON extraction accuracy was around 63-65%.

Record linkage accuracy was 81-94%.

Abstract

This research digitizes and analyzes the Leidse hoogleraren en lectoren 1575-1815 books written between 1983 and 1985, which contain biographic data about professors and curators of Leiden University. It addresses the central question: how can we design an automated pipeline that integrates OCR, LLM-based interpretation, and database linking to harmonize data from historical document images with existing high-quality database records? We applied OCR techniques, generative AI decoding constraints that structure data extraction, and database linkage methods to process typewritten historical records into a digital format. OCR achieved a Character Error Rate (CER) of 1.08 percent and a Word Error Rate (WER) of 5.06 percent, while JSON extraction from OCR text achieved an average accuracy of 63 percent and, based on annotated OCR, 65 percent. This indicates that generative AI somewhat corrects low OCR performance. Our record linkage algorithm linked annotated JSON files with 94% accuracy and OCR-derived JSON files with 81%. This study contributes to digital humanities research by offering an automated pipeline for interpreting digitized historical documents, addressing challenges like layout variability and terminology differences, and exploring the applicability and strength of an advanced generative AI model.