Talk:Embryology Image Tutorial

From Embryology
About Discussion Pages  
Mark Hill.jpg
On this website the Discussion Tab or "talk pages" for a topic has been used for several purposes:
  1. References - recent and historic that relates to the topic
  2. Additional topic information - currently prepared in draft format
  3. Links - to related webpages
  4. Topic page - an edit history as used on other Wiki sites
  5. Lecture/Practical - student feedback
  6. Student Projects - online project discussions.
Links: Pubmed Most Recent | Reference Tutorial | Journal Searches

Glossary Links

Glossary: A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Numbers | Symbols | Term Link

Cite this page: Hill, M.A. (2019, August 21) Embryology Embryology Image Tutorial. Retrieved from


Figure text extraction in biomedical literature

Kim D, Yu H.

PLoS One. 2011 Jan 13;6(1):e15338.


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 ( 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.

PMID: 21249186


Automatic figure ranking and user interfacing for intelligent figure search

Yu H, Liu F, Ramesh BP. PLoS One. 2010 Oct 7;5(10):e12983. PMID: 20949102


Are figure legends sufficient? Evaluating the contribution of associated text to biomedical figure comprehension

Yu H, Agarwal S, Johnston M, Cohen A. J Biomed Discov Collab. 2009 Jan 6;4:1. PMID: 19126221

FigSum: automatically generating structured text summaries for figures in biomedical literature

Agarwal S, Yu H. AMIA Annu Symp Proc. 2009 Nov 14;2009:6-10. PMID: 20351812