Creating a Historical Migration Dataset from Finnish Church Records, 1800-1920

TL;DR

This paper details the creation of a comprehensive, structured dataset of Finnish internal migration from 1800 to 1920, derived from digitized church records using a deep learning pipeline, enabling new research in historical demographics.

Contribution

It introduces an automated deep learning method to extract structured migration data from handwritten archival records at scale, a novel approach for historical demographic research.

Findings

Over six million migration entries extracted

Automated deep learning pipeline successfully applied to all images

Dataset enables studies on migration, urbanization, and disease spread

Abstract

This article presents a large-scale effort to create a structured dataset of internal migration in Finland between 1800 and 1920 using digitized church moving records. These records, maintained by Evangelical-Lutheran parishes, document the migration of individuals and families and offer a valuable source for studying historical demographic patterns. The dataset includes over six million entries extracted from approximately 200,000 images of handwritten migration records. The data extraction process was automated using a deep learning pipeline that included layout analysis, table detection, cell classification, and handwriting recognition. The complete pipeline was applied to all images, resulting in a structured dataset suitable for research. The dataset can be used to study internal migration, urbanization, and family migration, and the spread of disease in preindustrial Finland. A case study from the Elim\"aki parish shows how local migration histories can be reconstructed. The work demonstrates how large volumes of handwritten archival material can be transformed into structured data to support historical and demographic research.