  The EXO-POPP project aims to establish a comprehensive database comprising
300,000 marriage records from Paris and its suburbs, spanning the years 1880 to
1940, which are preserved in over 130,000 scans of double pages. Each marriage
record may encompass up to 118 distinct types of information that require
extraction from plain text. In this paper, we introduce the M-POPP dataset, a
subset of the M-POPP database with annotations for full-page text recognition
and information extraction in both handwritten and printed documents, and which
is now publicly available. We present a fully end-to-end architecture adapted
from the DAN, designed to perform both handwritten text recognition and
information extraction directly from page images without the need for explicit
segmentation. We showcase the information extraction capabilities of this
architecture by achieving a new state of the art for full-page Information
Extraction on Esposalles and we use this architecture as a baseline for the
M-POPP dataset. We also assess and compare how different encoding strategies
for named entities in the text affect the performance of jointly recognizing
handwritten text and extracting information, from full pages.
