# Processing the structure of documents: Logical Layout Analysis of historical newspapers in French

arXiv: https://arxiv.org/abs/2202.08125 · HTML: https://mlsanity.com/paper/2202.08125/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [A Large Dataset of Historical Japanese Documents with Complex Layouts](https://mlsanity.com/paper/2004.08686/) (2020)
- [Convolutional Neural Networks for Page Segmentation of Historical Document Images](https://mlsanity.com/paper/1704.01474/) (2017)
- [Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers](https://mlsanity.com/paper/2002.06144/) (2020)
- [Persian Heritage Image Binarization Competition (PHIBC 2012)](https://mlsanity.com/paper/1306.6263/) (2013)

## 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- [Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents](https://mlsanity.com/paper/2504.00414/) (2025)
- [Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction](https://mlsanity.com/paper/2402.13906/) (2024)
- [Understanding Archives: Towards New Research Interfaces Relying on the Semantic Annotation of Documents](https://mlsanity.com/paper/2403.19201/) (2024)
- [Pair-Level Essay-Scale Republication and Reuse from Fragmented Historical Text Reuse: A Workflow Study on Eighteenth-Century Books and Newspapers](https://mlsanity.com/paper/2608.27343/) (2026)

## Abstract (arXiv)

Background. In recent years, libraries and archives led important digitisation campaigns that opened the access to vast collections of historical documents. While such documents are often available as XML ALTO documents, they lack information about their logical structure. In this paper, we address the problem of Logical Layout Analysis applied to historical documents in French. We propose a rule-based method, that we evaluate and compare with two Machine-Learning models, namely RIPPER and Gradient Boosting. Our data set contains French newspapers, periodicals and magazines, published in the first half of the twentieth century in the Franche-Comt'e Region. Results. Our rule-based system outperforms the two other models in nearly all evaluations. It has especially better Recall results, indicating that our system covers more types of every logical label than the other two models. When comparing RIPPER with Gradient Boosting, we can observe that Gradient Boosting has better Precision scores but RIPPER has better Recall scores. Conclusions. The evaluation shows that our system outperforms the two Machine Learning models, and provides significantly higher Recall. It also confirms that our system can be used to produce annotated data sets that are large enough to envisage Machine Learning or Deep Learning approaches for the task of Logical Layout Analysis. Combining rules and Machine Learning models into hybrid systems could potentially provide even better performances. Furthermore, as the layout in historical documents evolves rapidly, one possible solution to overcome this problem would be to apply Rule Learning algorithms to bootstrap rule sets adapted to different publication periods.