# Diachronic Document Dataset for Semantic Layout Analysis

arXiv: https://arxiv.org/abs/2411.10068 · HTML: https://mlsanity.com/paper/2411.10068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020)
- [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://mlsanity.com/paper/2206.01062/) (2022)
- [DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception](https://mlsanity.com/paper/2410.12628/) (2024)
- [HORAE: an annotated dataset of books of hours](https://mlsanity.com/paper/2012.00351/) (2020)
- [Page Layout Analysis of Text-heavy Historical Documents: a Comparison of Textual and Visual Approaches](https://mlsanity.com/paper/2212.13924/) (2022)

## Built on by (most cited first, 1 of 1)

- [Thinking with Anchors: Grounded and Efficient Document Reasoning](https://mlsanity.com/paper/2608.04424/) (2026)

## Abstract (arXiv)

We present a novel, open-access dataset designed for semantic layout analysis, built to support document recreation workflows through mapping with the Text Encoding Initiative (TEI) standard. This dataset includes 7,254 annotated pages spanning a large temporal range (1600-2024) of digitised and born-digital materials across diverse document types (magazines, papers from sciences and humanities, PhD theses, monographs, plays, administrative reports, etc.) sorted into modular subsets. By incorporating content from different periods and genres, it addresses varying layout complexities and historical changes in document structure. The modular design allows domain-specific configurations. We evaluate object detection models on this dataset, examining the impact of input size and subset-based training. Results show that a 1280-pixel input size for YOLO is optimal and that training on subsets generally benefits from incorporating them into a generic model rather than fine-tuning pre-trained weights.