# Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks

arXiv: https://arxiv.org/abs/2012.14163 · HTML: https://mlsanity.com/paper/2012.14163/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition](https://mlsanity.com/paper/2203.12273/) (2022)
- [Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods](https://mlsanity.com/paper/2203.12346/) (2022)
- [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)
- [Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach](https://mlsanity.com/paper/2401.07787/) (2024)
- [Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data](https://mlsanity.com/paper/2107.06777/) (2021)
- [Including Keyword Position in Image-based Models for Act Segmentation of Historical Registers](https://mlsanity.com/paper/2109.08477/) (2021)
- [Handwritten Text Recognition from Crowdsourced Annotations](https://mlsanity.com/paper/2306.10878/) (2023)
- [Confidence Estimation for Object Detection in Document Images](https://mlsanity.com/paper/2208.13391/) (2022)
- [Quid est VERITAS? A Modular Framework for Archival Document Analysis](https://mlsanity.com/paper/2603.28108/) (2026)
- [Impact of Iterative Fine-Tuning on Transcription Accuracy in Complex Historical Sanskrit Manuscripts](https://mlsanity.com/paper/2608.18696/) (2026)

## Abstract (arXiv)

In this paper, we introduce a fully convolutional network for the document layout analysis task. While state-of-the-art methods are using models pre-trained on natural scene images, our method Doc-UFCN relies on a U-shaped model trained from scratch for detecting objects from historical documents. We consider the line segmentation task and more generally the layout analysis problem as a pixel-wise classification task then our model outputs a pixel-labeling of the input images. We show that Doc-UFCN outperforms state-of-the-art methods on various datasets and also demonstrate that the pre-trained parts on natural scene images are not required to reach good results. In addition, we show that pre-training on multiple document datasets can improve the performances. We evaluate the models using various metrics to have a fair and complete comparison between the methods.