# Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts

arXiv: https://arxiv.org/abs/1906.11894 · HTML: https://mlsanity.com/paper/1906.11894/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

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

- [Page Layout Analysis System for Unconstrained Historic Documents](https://mlsanity.com/paper/2102.11838/) (2021)
- [Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs](https://mlsanity.com/paper/2103.08236/) (2021)
- [Impact of Ground Truth Quality on Handwriting Recognition](https://mlsanity.com/paper/2312.09037/) (2023)
- [Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts](https://mlsanity.com/paper/2108.09436/) (2021)
- [Few-Shot Connectivity-Aware Text Line Segmentation in Historical Documents](https://mlsanity.com/paper/2508.19162/) (2025)
- [Unsupervised learning of text line segmentation by differentiating coarse patterns](https://mlsanity.com/paper/2105.09405/) (2021)
- [LineCounter: Learning Handwritten Text Line Segmentation by Counting](https://mlsanity.com/paper/2105.11307/) (2021)
- [DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis](https://mlsanity.com/paper/2201.08295/) (2022)

## Abstract (arXiv)

This paper introduces a new way for text-line extraction by integrating deep-learning based pre-classification and state-of-the-art segmentation methods. Text-line extraction in complex handwritten documents poses a significant challenge, even to the most modern computer vision algorithms. Historical manuscripts are a particularly hard class of documents as they present several forms of noise, such as degradation, bleed-through, interlinear glosses, and elaborated scripts. In this work, we propose a novel method which uses semantic segmentation at pixel level as intermediate task, followed by a text-line extraction step. We measured the performance of our method on a recent dataset of challenging medieval manuscripts and surpassed state-of-the-art results by reducing the error by 80.7%. Furthermore, we demonstrate the effectiveness of our approach on various other datasets written in different scripts. Hence, our contribution is two-fold. First, we demonstrate that semantic pixel segmentation can be used as strong denoising pre-processing step before performing text line extraction. Second, we introduce a novel, simple and robust algorithm that leverages the high-quality semantic segmentation to achieve a text-line extraction performance of 99.42% line IU on a challenging dataset.