Large Scale Genealogical Information Extraction From Handwritten Quebec Parish Records

Abstract

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.

Text Line Detection in Historical Index Tables: Evaluations on a New French PArish REcord Survey Dataset (PARES)

“…The aforementioned datasets and their corresponding projects center around two primary challenges: analysing document layout and recognizing handwritten text. On another hand, while not being a table dataset, SIMARA [33] is a dataset of handwritten archive finding aids, comprised of metadata describing historical archives. Finding aids are handwritten and feature the same scientific challenges regarding handwritten text recognition.…”

“…We aim at localizing text regions to perform text extraction and named entity recognition before indexing them. This is where the added value resides for archives and digital libraries [33], as it can potentially serve as finding aids for census tables and directly contribute to demographic studies. As a result, the detection of text lines is the baseline task we perform on this dataset.…”

“…The detection of text lines has been widely explored in historical manuscript text books [26,9] and other historical documents of different natures, such as newspapers [25], meteorological tables [1] finding aids [33], as well as many other supports. With index tables, one can consider the issue as a two-class image segmentation task: we separate text lines from the background.…”