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Segmentation and detection of text in document imagesZelenika, Darko ; Povh, Janez, 1973- ; Ženko, BernardText detection in document images plays an important role in optical character recognition systems and is a challenging task. The proposed text detection method uses self-adjusting bottom-up ... segmentation algorithm to segment a document image into a set of connected documents. The segmented connected components are then described in terms of 27 features and a machine learning algorithm is used to classify these components as text or non-text. We have collected a dataset (called ASTRoID), which contains 500 images of text blocks and 500 images of non-text blocks in order to test the method. We impirically compare performance of the proposed text detection method with seven different machine learning algorithms; the best performance is obtained with the radial support vector machine.Source: SOR '15 proceedings (Str. 155-160)Type of material - conference contributionPublish date - 2015Language - englishCOBISS.SI-ID - 17624665
Author
Zelenika, Darko |
Povh, Janez, 1973- |
Ženko, Bernard
Topics
text detection |
document segmentation |
text/non-text |
classification |
machine learning
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Database name | Field | Year |
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Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
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Zelenika, Darko | |
Povh, Janez, 1973- | 22649 |
Ženko, Bernard | 22279 |
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