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Erschienen in:
New Ordering-Based Pruning Metrics for Ensembles of Classifiers in Imbalanced Datasets Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Text Detection in Document Images by Machine Learning Algorithms

verfasst von : Darko Zelenika, Janez Povh, Bernard Ženko

Erschienen in: Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Verlag: Springer International Publishing

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Abstract

In the proposed paper, we consider a problem of text detection in document images. This problem plays an important role in OCR systems and is a challenging task. In the first step of our proposed text detection approach, we use a self-adjusting bottom-up segmentation algorithm to segment a document image into a set of connected components (CCs). The segmentation algorithm is based on the Sobel edge detection method. In the second step, CCs are described in terms of 27 features and a machine learning algorithm is then used to classify the CCs as text or nontext. For testing the approach, we have collected a dataset (ASTRoID), which contains 500 images of text blocks and 500 images of nontext blocks. We empirically compare performance of the proposed text detection method when using seven different machine learning algorithms.

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Vorheriges Kapitel Detection of Elongated Structures with Hierarchical Active Partitions and CEC-Based Image Representation
Nächstes Kapitel Blind Source Separation for Improved Load Forecasting on Individual Household Level
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Metadaten
Titel
Text Detection in Document Images by Machine Learning Algorithms
verfasst von
Darko Zelenika
Janez Povh
Bernard Ženko
Copyright-Jahr
2016
Buch
Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Print ISBN: 978-3-319-26225-3

Electronic ISBN: 978-3-319-26227-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-26227-7_16