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

Source: [Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts](<https://www.sota2.com/research/paper/68fa65f41e209131cea0d65e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T03:08:45.058Z

Authors: Michele Alberti, Lars V\\"ogtlin, Vinaychandran Pondenkandath, Mathias Seuret, Rolf Ingold, Marcus Liwicki

Published: 2019-06-11T11:06:43.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Text-line extraction on DIVA-HisDB](<https://www.sota2.com/research/sota/text-line-extraction-on-diva-hisdb.md>): Text-line extraction; DIVA-HisDB; Line IU: 99.42.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.11894>)

[PDF](<https://arxiv.org/pdf/1906.11894.pdf>)

[Implementation](<https://github.com/DIVA-DIA/Text-Line-Segmentation-Method-for-Medieval-Manuscripts>)