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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (6): 35-42    DOI: 10.11925/infotech.2096-3467.2019.1330
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Constructing Data Set for Location Annotations of Academic Literature Figures and Tables
Yu Fengchang,Lu Wei()
School of Information Management, Wuhan University, Wuhan 430072, China
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[Objective] This study proposes a size-adaptive template matching algorithm to quickly construct large-scale data set for academic literature figure and table positions. [Methods] First, we used the PubMed Open Access database to retrieve documents with figure/table images, and parsed their contents. Then, we matched document pages and pictures to extract their features. Finally, we identified the figure/table positions based on matched feature points. [Results] The proposed method’s precision and F1 value reached 98.87% and 97.44%, respectively. [Limitations] We only used simple keywords to match literature pages and figure/table pictures. [Conclusions] ;The proposed algorithm could quickly construct data set for chart positions in academic literature.

Key wordsData Set Annotation      Template Matching      Academic Literature     
Received: 13 December 2019      Published: 23 April 2020
ZTFLH:  TP393  
Corresponding Authors: Lu Wei     E-mail:

Cite this article:

Yu Fengchang,Lu Wei. Constructing Data Set for Location Annotations of Academic Literature Figures and Tables. Data Analysis and Knowledge Discovery, 2020, 4(6): 35-42.

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A Schematic Diagram of Annotation Mode
Flow Chart of Size Adaptive Template Matching Algorithm
Examples of Figure Annotation
性能指标 精确率 召回率 F1
结果 98.87% 96.06% 97.44%
Results of Annotation Experiment
An Example of Error in Correspondence Between Template and Paper Page
An Example of Matching Error Caused by Lack of Corner Features
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