  This paper presents a complete processing workflow for extracting information
from French census lists from 1836 to 1936. These lists contain information
about individuals living in France and their households. We aim at extracting
all the information contained in these tables using automatic handwritten table
recognition. At the end of the Socface project, in which our work is taking
place, the extracted information will be redistributed to the departmental
archives, and the nominative lists will be freely available to the public,
allowing anyone to browse hundreds of millions of records. The extracted data
will be used by demographers to analyze social change over time, significantly
improving our understanding of French economic and social structures. For this
project, we developed a complete processing workflow: large-scale data
collection from French departmental archives, collaborative annotation of
documents, training of handwritten table text and structure recognition models,
and mass processing of millions of images. We present the tools we have
developed to easily collect and process millions of pages. We also show that it
is possible to process such a wide variety of tables with a single table
recognition model that uses the image of the entire page to recognize
information about individuals, categorize them and automatically group them
into households. The entire process has been successfully used to process the
documents of a departmental archive, representing more than 450,000 images.
