# Normalized vs Diplomatic Annotation: A Case Study of Automatic Information Extraction from Handwritten Uruguayan Birth Certificates

arXiv: https://arxiv.org/abs/2507.08636 · HTML: https://mlsanity.com/paper/2507.08636/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding](https://mlsanity.com/paper/2210.06155/) (2022)
- [DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition](https://mlsanity.com/paper/2203.12273/) (2022)
- [End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network](https://mlsanity.com/paper/2012.03868/) (2020)
- [Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention](https://mlsanity.com/paper/1604.03286/) (2016)
- [dhSegment: A generic deep-learning approach for document segmentation](https://mlsanity.com/paper/1804.10371/) (2018)
- [Full Page Handwriting Recognition via Image to Sequence Extraction](https://mlsanity.com/paper/2103.06450/) (2021)
- [docExtractor: An off-the-shelf historical document element extraction](https://mlsanity.com/paper/2012.08191/) (2020)
- [LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding](https://mlsanity.com/paper/2305.18721/) (2023)
- [Key-value information extraction from full handwritten pages](https://mlsanity.com/paper/2304.13530/) (2023)
- [Large Scale Genealogical Information Extraction From Handwritten Quebec Parish Records](https://mlsanity.com/paper/2304.14044/) (2023)
- [The Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censuses](https://mlsanity.com/paper/2404.18706/) (2024)

## Built on by (most cited first, 3 of 3)

- [Improving OCR using internal document redundancy](https://mlsanity.com/paper/2508.14557/) (2025)
- [FP-THD: Full page transcription of historical documents](https://mlsanity.com/paper/2601.17040/) (2026)
- [Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin](https://mlsanity.com/paper/2602.13905/) (2026)

## Abstract (arXiv)

This study evaluates the recently proposed Document Attention Network (DAN) for extracting key-value information from Uruguayan birth certificates, handwritten in Spanish. We investigate two annotation strategies for automatically transcribing handwritten documents, fine-tuning DAN with minimal training data and annotation effort. Experiments were conducted on two datasets containing the same images (201 scans of birth certificates written by more than 15 different writers) but with different annotation methods. Our findings indicate that normalized annotation is more effective for fields that can be standardized, such as dates and places of birth, whereas diplomatic annotation performs much better for fields containing names and surnames, which can not be standardized.