A Robust and Binarization-Free Approach for Text Line Detection in Historical Documents

Gruuening, Tobias; Leifert, Gundram; Strauß, Tobias; Labahn, Roger

doi:10.1109/icdar.2017.47

Cited by 27 publications

(18 citation statements)

References 24 publications

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“…Typically, layout analysis algorithms directly work on the input image I or on a binarized version of it [17,23,29,30,31,33]. Instead, we employ a more goal-oriented transformation of the input image utilizing a neural network, which is trained in a supervised manner to assign a certain class to each pixel like in [21,40,41].…”

Section: Stage I: Aru-netmentioning

confidence: 99%

A two-stage method for text line detection in historical documents

Grüning

Leifert

Strauß

et al. 2019

IJDAR

Self Cite

117

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This work presents a two-stage text line detection method for historical documents. Each detected text line is represented by its baseline. In a first stage, a deep neural network called ARU-Net labels pixels to belong to one of the three classes: baseline, separator or other. The separator class marks beginning and end of each text line. The ARU-Net is trainable from scratch with manageably few manually annotated example images (less than 50). This is achieved by utilizing data augmentation strategies. The network predictions are used as input for the second stage which performs a bottom-up clustering to build baselines. The developed method is capable of handling complex layouts as well as curved and arbitrarily oriented text lines. It substantially outperforms current state-of-the-art approaches. For example, for the complex track of the cBAD: IC-DAR2017 Competition on Baseline Detection the Fvalue is increased from 0.859 to 0.922. The framework to train and run the ARU-Net is open source.

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Section: Stage I: Aru-netmentioning

confidence: 99%

A two-stage method for text line detection in historical documents

Grüning

Leifert

Strauß

et al. 2019

IJDAR

Self Cite

117

View full text Add to dashboard Cite

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“…The closest work related to our method are multi-step methods, presented by Pastor et al [14] and Gruüning et al [15]. The former employs a multi-stage deep learning approach to detect text regions followed by watershed-transform as post-processing step.…”

Section: Related Workmentioning

confidence: 99%

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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“…21 The technical partner for the development of the layout analysis and training and recognition software is the CITlab 22 team at the University of Rostock whose approach performed best on the sub task of the detection of baselines, that is the line supporting the main bodies of characters within a text line, at a competition layout analysis for challenging medieval manuscripts at ICDAR2017 [28]. Several related publications are available (see for example [29,30] for layout analysis and [31] for HTR) but to the best of our knowledge the exact state of the software actually incorporated in Transkribus is not publicly known. Therefore, the best source for results seems a recently (May 2019) published talk 23 which biefly sums up some evaluations: After training on close to 36,000 words corresponding to 182 pages a CER of 3.1% and a WER of 13.1% was achieved on a dataset from the 18 th century written by a single writer in German.…”

Section: Transkribusmentioning

confidence: 99%

OCR4all—An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings

et al. 2019

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Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout recognition and segmentation, character recognition and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. A comfortable GUI allows error corrections not only in the final output, but already in early stages to minimize error propagations. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. Experiments showed that users with minimal or no experience were able to capture the text of even the earliest printed books with manageable effort and great quality, achieving excellent character error rates (CERs) below 0.5%. The fully automated application on 19 th century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings.

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A Robust and Binarization-Free Approach for Text Line Detection in Historical Documents

Cited by 27 publications

References 24 publications

A two-stage method for text line detection in historical documents

A two-stage method for text line detection in historical documents

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

OCR4all—An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings

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