# Page Layout Analysis of Text-heavy Historical Documents: a Comparison of Textual and Visual Approaches

arXiv: https://arxiv.org/abs/2212.13924 · HTML: https://mlsanity.com/paper/2212.13924/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [DiT: Self-supervised Pre-training for Document Image Transformer](https://mlsanity.com/paper/2203.02378/) (2022)
- [Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network](https://mlsanity.com/paper/1706.02337/) (2017)
- [Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers](https://mlsanity.com/paper/2002.06144/) (2020)
- [You Actually Look Twice At it (YALTAi): using an object detection approach instead of region segmentation within the Kraken engine](https://mlsanity.com/paper/2207.11230/) (2022)

## Built on by (most cited first, 7 of 7)

- [Yes but.. Can ChatGPT Identify Entities in Historical Documents?](https://mlsanity.com/paper/2303.17322/) (2023)
- [Structure-Aware Text Recognition for Ancient Greek Critical Editions](https://mlsanity.com/paper/2603.02803/) (2026)
- [Reading the unreadable: Creating a dataset of 19th century English newspapers using image-to-text language models](https://mlsanity.com/paper/2502.14901/) (2025)
- [Diachronic Document Dataset for Semantic Layout Analysis](https://mlsanity.com/paper/2411.10068/) (2024)
- [Studying Illustrations in Manuscripts: An Efficient Deep-Learning Approach](https://mlsanity.com/paper/2601.05269/) (2025)
- [Semantic-Guided Reading Order Reconstruction in Historical Armenian Newspapers with LLMs](https://mlsanity.com/paper/2607.00596/) (2026)
- [The Patrologia Graeca Corpus: OCR, Annotation, and Open Release of Noisy Nineteenth-Century Polytonic Greek Editions](https://mlsanity.com/paper/2603.09470/) (2026)

## Abstract (arXiv)

Page layout analysis is a fundamental step in document processing which enables to segment a page into regions of interest. With highly complex layouts and mixed scripts, scholarly commentaries are text-heavy documents which remain challenging for state-of-the-art models. Their layout considerably varies across editions and their most important regions are mainly defined by semantic rather than graphical characteristics such as position or appearance. This setting calls for a comparison between textual, visual and hybrid approaches. We therefore assess the performances of two transformers (LayoutLMv3 and RoBERTa) and an objection-detection network (YOLOv5). If results show a clear advantage in favor of the latter, we also list several caveats to this finding. In addition to our experiments, we release a dataset of ca. 300 annotated pages sampled from 19th century commentaries.