The PARES Database: Information Extraction over Historical Parish Records

Abstract Historical census records convey information that is key to perform genealogical research and demographic studies. Given the large number of documents of this type that exist, it is crucial to research methods that allow the automatic extraction of information from this type of document. In this work, we present a new corpus of this kind, comprising 535 historical census tables from French archives. Alongside this dataset, we have assessed three different baseline methods for information extraction. The first two methods employ a traditional sequential approach, where table rows are detected before extracting information. The third baseline uses an end-to-end model that directly extracts information from the table images without prior row detection. Our results demonstrate the effectiveness of all three baselines in tackling the information extraction task.

Authors

Institutions

Publication Details

Journal
International Journal on Document Analysis and Recognition (IJDAR)
Published
2025-05-28
DOI
https://doi.org/10.1007/s10032-025-00531-z
Primary Topic
Web Data Mining and Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The PARES Database: Information Extraction over Historical Parish Records

Alejandro H. Toselli, José Onofre Montesa Andrés, Mickaël Coustaty, Enrique Vidal et al.
International Journal on Document Analysis and Recognition (IJDAR)
Web Data Mining and Analysis
article

The PARES Database: Information Extraction over Historical Parish Records

Alejandro H. Toselli, José Onofre Montesa Andrés, Mickaël Coustaty, Enrique Vidal, Solène Tarride, C. Edward Wall
article en

Abstract

Abstract Historical census records convey information that is key to perform genealogical research and demographic studies. Given the large number of documents of this type that exist, it is crucial to research methods that allow the automatic extraction of information from this type of document. In this work, we present a new corpus of this kind, comprising 535 historical census tables from French archives. Alongside this dataset, we have assessed three different baseline methods for information extraction. The first two methods employ a traditional sequential approach, where table rows are detected before extracting information. The third baseline uses an end-to-end model that directly extracts information from the table images without prior row detection. Our results demonstrate the effectiveness of all three baselines in tackling the information extraction task.

International Journal on Document Analysis and Recognition (IJDAR)Vol. 28(3)
Universitat Politècnica de València (ES), La Rochelle Université (FR)
Universitat Politècnica de València
Openalex Percentile: Top 84%
Web Data Mining and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.