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.

EXO-POPP 项目旨在建立一个包含法国巴黎及其郊区 1880 年至 1940 年之间的 30 万份婚姻记录的综合数据库，其中包括超过 13 万多个扫描的双页图像。该论文介绍了 M-POPP 数据集，这是 M-POPP 数据库的一个子集，用于手写和印刷文件的全页文本识别和信息提取，并已公开提供。通过从页面图像直接进行手写文本识别和信息提取，我们提出了一种完整的端到端架构，该架构是从 DAN 进行了适应。我们通过在 Esposalles 上实现全页信息提取的最新技术水平来展示该架构的信息提取能力，并将该架构作为 M-POPP 数据集的基准。我们还评估并比较了不同的命名实体编码策略对识别手写文本和提取信息的全页性能的影响。