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International Journal on Document Analysis and Recognition (IJDAR)
Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) 3/2019

23.07.2019 | Special Issue Paper

A two-stage method for text line detection in historical documents

verfasst von: Tobias Grüning, Gundram Leifert, Tobias Strauß, Johannes Michael, Roger Labahn

Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) | Ausgabe 3/2019

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Abstract

This work presents a two-stage text line detection method for historical documents.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 and 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 (\(<\,50\)). 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: ICDAR2017 Competition on Baseline Detection the F value is increased from 0.859 to 0.922. The framework to train and run the ARU-Net is open source.
Vorheriger Artikel Comic MTL: optimized multi-task learning for comic book image analysis
Nächster Artikel On optimal stopping strategies for text recognition in a video stream as an application of a monotone sequential decision model

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Fußnoten
1
Optical Character Recognition + Handwritten Text Recognition.
 
9
A separable MDLSTM layer is a concatenation of two (x- and y-direction) BLSTM layers.
 
10
The competition training data were not available to the authors.
 
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Metadaten
Titel
A two-stage method for text line detection in historical documents
verfasst von
Tobias Grüning
Gundram Leifert
Tobias Strauß
Johannes Michael
Roger Labahn
Publikationsdatum
23.07.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal on Document Analysis and Recognition (IJDAR) / Ausgabe 3/2019
Print ISSN: 1433-2833
Elektronische ISSN: 1433-2825
DOI
https://doi.org/10.1007/s10032-019-00332-1

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