# Large Scale Genealogical Information Extraction From Handwritten Quebec Parish Records

arXiv: https://arxiv.org/abs/2304.14044 · HTML: https://mlsanity.com/paper/2304.14044/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 49 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Graph Convolution for Multimodal Information Extraction from Visually Rich Documents](https://mlsanity.com/paper/1903.11279/) (2019)
- [PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks](https://mlsanity.com/paper/2004.07464/) (2020)
- [Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution](https://mlsanity.com/paper/2102.06732/) (2021)
- [DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition](https://mlsanity.com/paper/2203.12273/) (2022)
- [Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention](https://mlsanity.com/paper/1604.03286/) (2016)
- [dhSegment: A generic deep-learning approach for document segmentation](https://mlsanity.com/paper/1804.10371/) (2018)
- [A Two-Stage Method for Text Line Detection in Historical Documents](https://mlsanity.com/paper/1802.03345/) (2018)
- [docExtractor: An off-the-shelf historical document element extraction](https://mlsanity.com/paper/2012.08191/) (2020)
- [READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents](https://mlsanity.com/paper/1705.03311/) (2017)
- [Transformer-Based Approach for Joint Handwriting and Named Entity Recognition in Historical documents](https://mlsanity.com/paper/2112.04189/) (2021)
- [A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages](https://mlsanity.com/paper/1912.10016/) (2019)
- [Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks](https://mlsanity.com/paper/2012.14163/) (2020)
- [Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model](https://mlsanity.com/paper/1803.06252/) (2018)
- [Importance of Textlines in Historical Document Classification](https://mlsanity.com/paper/2201.09575/) (2022)
- [Record Counting in Historical Handwritten Documents with Convolutional Neural Networks](https://mlsanity.com/paper/1610.07393/) (2016)
- [Including Keyword Position in Image-based Models for Act Segmentation of Historical Registers](https://mlsanity.com/paper/2109.08477/) (2021)

## Built on by (most cited first, 4 of 4)

- [The Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censuses](https://mlsanity.com/paper/2404.18706/) (2024)
- [CM1 -- A Dataset for Evaluating Few-Shot Information Extraction with Large Vision Language Models](https://mlsanity.com/paper/2505.04214/) (2025)
- [Normalized vs Diplomatic Annotation: A Case Study of Automatic Information Extraction from Handwritten Uruguayan Birth Certificates](https://mlsanity.com/paper/2507.08636/) (2025)
- [Reading Order Independent Metrics for Information Extraction in Handwritten Documents](https://mlsanity.com/paper/2404.18664/) (2024)

## Abstract (arXiv)

This paper presents a complete workflow designed for extracting information from Quebec handwritten parish registers. The acts in these documents contain individual and family information highly valuable for genetic, demographic and social studies of the Quebec population. From an image of parish records, our workflow is able to identify the acts and extract personal information. The workflow is divided into successive steps: page classification, text line detection, handwritten text recognition, named entity recognition and act detection and classification. For all these steps, different machine learning models are compared. Once the information is extracted, validation rules designed by experts are then applied to standardize the extracted information and ensure its consistency with the type of act (birth, marriage, and death). This validation step is able to reject records that are considered invalid or merged. The full workflow has been used to process over two million pages of Quebec parish registers from the 19-20th centuries. On a sample comprising 65% of registers, 3.2 million acts were recognized. Verification of the birth and death acts from this sample shows that 74% of them are considered complete and valid. These records will be integrated into the BALSAC database and linked together to recreate family and genealogical relations at large scale.