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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Songhua Xu Krauthammer, M. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Pathology, Yale University School of Medicine 310 Cedar Street, New Haven, CT 06520-8023, USA (Krauthammer, M.) || Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA (Songhua Xu) |
| Abstract | In this paper, we show that domain-optimized text detection in biomedical images is important for boosting text extraction recall via off-the-shelf OCR engines. Methodologically, we contrast OCR performance when processing raw biomedical images, compared to preprocessing those images, and performing OCR on detected image text regions only. To quantify OCR extraction results, we rely on a gold standard image text corpus with manually identified image text strings. To demonstrate the positive effect on biomedical image retrieval, we apply image text detection and extraction to a large corpus of biomedical images in the Yale Image Finder system. We show that improved text extraction results in the retrieval of a larger number of relevant images for a set of domain-relevant keyword searches. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 67232 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781612844114 |
| e-ISBN | 9781612844107 |
| DOI | 10.1109/BSEC.2011.5872319 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-03-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Gold Image retrieval Optical character recognition software Engines Biomedical imaging Guidelines Videos |
| Content Type | Text |
| Resource Type | Article |
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