# Multi-Task Handwritten Document Layout Analysis

arXiv: https://arxiv.org/abs/1806.08852 · HTML: https://mlsanity.com/paper/1806.08852/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2018.

## Built on by (most cited first, 8 of 8)

- [DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition](https://mlsanity.com/paper/2203.12273/) (2022)
- [Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction](https://mlsanity.com/paper/2410.21169/) (2024)
- [Page Layout Analysis System for Unconstrained Historic Documents](https://mlsanity.com/paper/2102.11838/) (2021)
- [A Survey of Historical Document Image Datasets](https://mlsanity.com/paper/2203.08504/) (2022)
- [Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method](https://mlsanity.com/paper/2209.09207/) (2022)
- [Evaluation of a Region Proposal Architecture for Multi-task Document Layout Analysis](https://mlsanity.com/paper/2106.11797/) (2021)
- [Digital Editions as Distant Supervision for Layout Analysis of Printed Books](https://mlsanity.com/paper/2112.12703/) (2021)
- [Semantic-Guided Reading Order Reconstruction in Historical Armenian Newspapers with LLMs](https://mlsanity.com/paper/2607.00596/) (2026)

## Abstract (arXiv)

Document Layout Analysis is a fundamental step in Handwritten Text Processing systems, from the extraction of the text lines to the type of zone it belongs to. We present a system based on artificial neural networks which is able to determine not only the baselines of text lines present in the document, but also performs geometric and logic layout analysis of the document. Experiments in three different datasets demonstrate the potential of the method and show competitive results with respect to state-of-the-art methods.