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.

本文介绍了一种用于从魁北克手写教区登记簿中提取信息的完整工作流程，该工作流程通过将文档图像转换为个人化的信息，使其适用于遗传、人口统计和社会研究。工作流程采用逐步方法，并采用不同的机器学习模型，同时由专家设计的验证规则用于标准化提取的信息并确保其一致性。该方法已被用于魁北克教区登记簿中超过 200 万个页面的处理，并将被整合到 BALSAC 数据库中进行家族和系谱关系的重建。