# End-to-end information extraction in handwritten documents: Understanding Paris marriage records from 1880 to 1940

arXiv: https://arxiv.org/abs/2404.19329 · HTML: https://mlsanity.com/paper/2404.19329/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition](https://mlsanity.com/paper/2203.12273/) (2022)
- [OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold](https://mlsanity.com/paper/2006.07491/) (2020)
- [End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network](https://mlsanity.com/paper/2012.03868/) (2020)
- [Transformer-Based Approach for Joint Handwriting and Named Entity Recognition in Historical documents](https://mlsanity.com/paper/2112.04189/) (2021)
- [A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages](https://mlsanity.com/paper/1912.10016/) (2019)
- [Key-value information extraction from full handwritten pages](https://mlsanity.com/paper/2304.13530/) (2023)
- [Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model](https://mlsanity.com/paper/1803.06252/) (2018)
- [Bench-Marking Information Extraction in Semi-Structured Historical Handwritten Records](https://mlsanity.com/paper/1807.06270/) (2018)

## Built on by (most cited first, 4 of 4)

- [Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents](https://mlsanity.com/paper/2504.00414/) (2025)
- [DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents](https://mlsanity.com/paper/2407.09103/) (2024)
- [Meta-DAN: towards an efficient prediction strategy for page-level handwritten text recognition](https://mlsanity.com/paper/2504.03349/) (2025)
- [Improving OCR using internal document redundancy](https://mlsanity.com/paper/2508.14557/) (2025)

## Abstract (arXiv)

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.