Department of Quantitative Health Sciences
Bioinformatics | Databases and Information Systems | Health Information Technology | Numerical Analysis and Scientific Computing | Statistics and Probability | Theory and Algorithms
Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information. A high-quality ground truth standard can greatly facilitate the development of an automated system. This article describes DeTEXT: A database for evaluating text extraction from biomedical literature figures. It is the first publicly available, human-annotated, high quality, and large-scale figure-text dataset with 288 full-text articles, 500 biomedical figures, and 9308 text regions. This article describes how figures were selected from open-access full-text biomedical articles and how annotation guidelines and annotation tools were developed. We also discuss the inter-annotator agreement and the reliability of the annotations. We summarize the statistics of the DeTEXT data and make available evaluation protocols for DeTEXT. Finally we lay out challenges we observed in the automated detection and recognition of figure text and discuss research directions in this area. DeTEXT is publicly available for downloading at http://prir.ustb.edu.cn/DeTEXT/.
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DOI of Published Version
PLoS One. 2015 May 7;10(5):e0126200. doi: 10.1371/journal.pone.0126200. eCollection 2015. Link to article on publisher's site
Yin X, Yang C, Pei W, Man H, Zhang J, Learned-Miller E, Yu H. (2015). DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures. Open Access Publications by UMass Chan Authors. https://doi.org/10.1371/journal.pone.0126200. Retrieved from https://escholarship.umassmed.edu/oapubs/2541
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