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1. Kim D, Yu H: Figure text extraction in biomedical literature. PLoS One; 2011 Jan 13;6(1):e15338
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Figure text extraction in biomedical literature.
  • BACKGROUND: Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge.
  • However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures.
  • Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures efficiently.
  • Since text frequently appears in figures, automatically extracting such text may assist the task of mining information from figures.
  • Little research, however, has been conducted exploring text extraction from biomedical figures.
  • METHODOLOGY: We first evaluated an off-the-shelf Optical Character Recognition (OCR) tool on its ability to extract text from figures appearing in biomedical full-text articles.
  • We then developed a Figure Text Extraction Tool (FigTExT) to improve the performance of the OCR tool for figure text extraction through the use of three innovative components: image preprocessing, character recognition, and text correction.
  • We first developed image preprocessing to enhance image quality and to improve text localization.
  • Then we adapted the off-the-shelf OCR tool on the improved text localization for character recognition.
  • Finally, we developed and evaluated a novel text correction framework by taking advantage of figure-specific lexicons.
  • RESULTS/CONCLUSIONS: The evaluation on 382 figures (9,643 figure texts in total) randomly selected from PubMed Central full-text articles shows that FigTExT performed with 84% precision, 98% recall, and 90% F1-score for text localization and with 62.5% precision, 51.0% recall and 56.2% F1-score for figure text extraction.
  • When limiting figure texts to those judged by domain experts to be important content, FigTExT performed with 87.3% precision, 68.8% recall, and 77% F1-score.
  • FigTExT significantly improved the performance of the off-the-shelf OCR tool we used, which on its own performed with 36.6% precision, 19.3% recall, and 25.3% F1-score for text extraction.
  • In addition, our results show that FigTExT can extract texts that do not appear in figure captions or other associated text, further suggesting the potential utility of FigTExT for improving figure search.

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  • (PMID = 21249186.001).
  • [ISSN] 1932-6203
  • [Journal-full-title] PloS one
  • [ISO-abbreviation] PLoS ONE
  • [Language] ENG
  • [Grant] United States / NCRR NIH HHS / RR / R21 RR024933; United States / NCRR NIH HHS / RR / 1R21RR024933
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC3020938
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