ICDAR2017 Competition on Layout Analysis for Challenging Medieval Manuscripts

Simistira, Fotini; Bouillon, Manuel; Seuret, Mathias; Würsch, Marcel; Alberti, Michele; Ingold, Rolf; Liwicki, Marcus

doi:10.1109/icdar.2017.223

Cited by 46 publications

(53 citation statements)

References 19 publications

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“…In this work, we mainly use the DIVA-HisDB dataset, which is a collection of three medieval manuscripts that have been selected for the high complexity of their layout [3]. Figure 2 shows some properties of the dataset that make it particularly challenging.…”

Section: A Diva-hisdbmentioning

confidence: 99%

“…The task considered in this work is text-line segmentation i.e. given an RGB image (Figure 1a), produce a list of polygons enclosing the text-lines in the document (Figure 1c); as proposed for Task-3 at the ICDAR2017 Competition on Layout Analysis for Challenging Medieval Manuscripts [3]. These polygons should be distinct, non-overlapping and nonconvex to be effective on the DIVA-HisDB dataset.…”

Section: Task: Text-line Segmentationmentioning

confidence: 99%

“…which delivers near state-of-the-art results [24], [3]. To run it we used the DeepDIVA deep-learning framework [25].…”

Section: A Semantic Segmentationmentioning

confidence: 99%

“…Text-line segmentation is a crucial part of document image processing and remains mainly unsolved, especially in documents with complex layouts [1], [2], [3]. While nearlyperfect commercial tools exist for scanned modern texts and historical printed texts with simple layouts [4], other documents (handwritten documents, ornamented documents, and advertisements) pose more challenges.…”

Section: Introductionmentioning

confidence: 99%

“…The second key contribution is introducing a novel simple algorithm that leverages this data preparation, achieving promising results on a challenging dataset of medieval manuscripts. Finally, we open-source 2 our 1 Note that at ICDAR 2017 Seuret et al [3] have used a preliminary version of our proposed method for demonstrating the possibility of classification as proxy task, however, this paper presents a more elaborated and robust method. 2 https://github.com/DIVA-DIA/Text-Line-Segmentation-Method-for-Medi eval-Manuscripts code such that other researchers can benefit from it.…”

Section: Introductionmentioning

confidence: 99%

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

Alberti

Vögtlin

Pondenkandath

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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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, bleedthrough, 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 highquality semantic segmentation to achieve a text-line extraction performance of 99.42% line IU on a challenging dataset.

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Section: A Diva-hisdbmentioning

confidence: 99%

Section: Task: Text-line Segmentationmentioning

confidence: 99%

“…which delivers near state-of-the-art results [24], [3]. To run it we used the DeepDIVA deep-learning framework [25].…”

Section: A Semantic Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Alberti

Vögtlin

Pondenkandath

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

Self Cite

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Simultaneous Detection of Regular Patterns in Ancient Manuscripts Using GAN-Based Deep Unsupervised Segmentation

Tamrin

Cheriet

2021

Pattern Recognition. ICPR International Workshops and Challenges

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Text Line Extraction Using Fully Convolutional Network and Energy Minimization

Barakat

Droby

Alaasam

et al. 2021

Lecture Notes in Computer Science

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Text lines are important parts of handwritten document images and easier to analyze by further applications. Despite recent progress in text line detection, text line extraction from a handwritten document remains an unsolved task. This paper proposes to use a fully convolutional network for text line detection and energy minimization for text line extraction. Detected text lines are represented by blob lines that strike through the text lines. These blob lines assist an energy function for text line extraction. The detection stage can locate arbitrarily oriented text lines.Furthermore, the extraction stage is capable of finding out the pixels of text lines with various heights and interline proximity independent of their orientations. Besides, it can finely split the touching and overlapping text lines without an orientation assumption. We evaluate the proposed method on VML-AHTE, VML-MOC, and Diva-HisDB datasets. The VML-AHTE dataset contains overlapping, touching and close text lines with rich diacritics. The VML-MOC dataset is very challenging by its multiply oriented and skewed text lines. The Diva-HisDB dataset exhibits distinct text line heights and touching text lines. The results demonstrate the effectiveness of the method despite various types of challenges, yet using the same parameters in all the experiments.

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ICDAR2017 Competition on Layout Analysis for Challenging Medieval Manuscripts

Cited by 46 publications

References 19 publications

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

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

Simultaneous Detection of Regular Patterns in Ancient Manuscripts Using GAN-Based Deep Unsupervised Segmentation

Text Line Extraction Using Fully Convolutional Network and Energy Minimization

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