# Key-value information extraction from full handwritten pages

arXiv: https://arxiv.org/abs/2304.13530 · HTML: https://mlsanity.com/paper/2304.13530/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [End-to-end Document Recognition and Understanding with Dessurt](https://mlsanity.com/paper/2203.16618/) (2022)
- [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- [Transformer-Based Approach for Joint Handwriting and Named Entity Recognition in Historical documents](https://mlsanity.com/paper/2112.04189/) (2021)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)
- [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, 9 of 9)

- [DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents](https://mlsanity.com/paper/2407.09103/) (2024)
- [Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library](https://mlsanity.com/paper/2404.18722/) (2024)
- [The Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censuses](https://mlsanity.com/paper/2404.18706/) (2024)
- [End-to-end information extraction in handwritten documents: Understanding Paris marriage records from 1880 to 1940](https://mlsanity.com/paper/2404.19329/) (2024)
- [Meta-DAN: towards an efficient prediction strategy for page-level handwritten text recognition](https://mlsanity.com/paper/2504.03349/) (2025)
- [CM1 -- A Dataset for Evaluating Few-Shot Information Extraction with Large Vision Language Models](https://mlsanity.com/paper/2505.04214/) (2025)
- [Normalized vs Diplomatic Annotation: A Case Study of Automatic Information Extraction from Handwritten Uruguayan Birth Certificates](https://mlsanity.com/paper/2507.08636/) (2025)
- [Reading Order Independent Metrics for Information Extraction in Handwritten Documents](https://mlsanity.com/paper/2404.18664/) (2024)
- [Revisiting N-Gram Models: Their Impact in Modern Neural Networks for Handwritten Text Recognition](https://mlsanity.com/paper/2404.19317/) (2024)

## Abstract (arXiv)

We propose a Transformer-based approach for information extraction from digitized handwritten documents. Our approach combines, in a single model, the different steps that were so far performed by separate models: feature extraction, handwriting recognition and named entity recognition. We compare this integrated approach with traditional two-stage methods that perform handwriting recognition before named entity recognition, and present results at different levels: line, paragraph, and page. Our experiments show that attention-based models are especially interesting when applied on full pages, as they do not require any prior segmentation step. Finally, we show that they are able to learn from key-value annotations: a list of important words with their corresponding named entities. We compare our models to state-of-the-art methods on three public databases (IAM, ESPOSALLES, and POPP) and outperform previous performances on all three datasets.