title: DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures.
abstract: 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/.
authors.affiliation: Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
authors.forename: Xu-Cheng
authors.initials: XC
authors.lastname: Yin
authors.affiliation: Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
authors.forename: Chun
authors.initials: C
authors.lastname: Yang
authors.affiliation: Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
authors.forename: Wei-Yi
authors.initials: WY
authors.lastname: Pei
authors.affiliation: School of Foreign Studies, University of Science and Technology Beijing, Beijing, China.
authors.forename: Haixia
authors.initials: H
authors.lastname: Man
authors.affiliation: Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
authors.affiliation: School of Computer Science, University of Massachusetts Amherst, MA, USA.
authors.forename: Erik
authors.initials: E
authors.lastname: Learned-Miller
authors.affiliation: School of Computer Science, University of Massachusetts Amherst, MA, USA; Department of Quantitative Health Sciences, University of Massachusetts Medical School, MA, USA.
authors.forename: Hong
authors.initials: H
authors.lastname: Yu
mesh_terms: D000595:Amino Acid Sequence; D016208:Databases, Factual; D008969:Molecular Sequence Data; D017386:Sequence Homology, Amino Acid