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J Biomed Inform. 2010 Dec;43(6):924-31. doi: 10.1016/j.jbi.2010.09.006. Epub 2010 Sep 29.

A new pivoting and iterative text detection algorithm for biomedical images.

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1
Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA.

Abstract

There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper's key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. We demonstrate that our projection histogram-based text detection approach is well suited for text detection in biomedical images, and that the iterative application of the algorithm boosts performance to an F score of .60. We provide a C++ implementation of our algorithm freely available for academic use.

PMID:
20887803
PMCID:
PMC3265968
DOI:
10.1016/j.jbi.2010.09.006
[Indexed for MEDLINE]
Free PMC Article
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