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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (1): 140-149    DOI: 10.11925/infotech.2096-3467.2020.0630
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Locating Academic Literature Figures and Tables with Geometric Object Clustering
Yu Fengchang,Cheng Qikai,Lu Wei()
School of Information Management, Wuhan University, Wuhan 430072, China
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[Objective] This paper tries to improve the recall of figures/tables from academic literature. [Methods] First, we extracted geometric objects from the PDF files of literature. Then, we obtained priori information on scopes of figures/tables from the perspectives of underlying coding analysis and image comprehension. Third, we merged the geometric objects using K-means. Finally, we reconstructed the text contents using heuristic algorithm to determine the locations of figures/tables. [Results] On the experimental dataset, the precision of the proposed algorithm reached 0.915 and the recall was 0.918. The precision level is close to the state-of-the-art algorithms and the recall value was improved by 0.193 (26.6% better than the existing ones). [Limitations] Documents with complex layouts and irregular use of symbols will generate errors. The determination of the clustering k value and the algorithm for text filtering could be improved. [Conclusions] The proposed algorithm effectively increases the recall of figures/tables from academic literature.

Key wordsAcademic Literature      Figures/Tables Localization      Clustering     
Received: 01 July 2020      Published: 29 October 2020
ZTFLH:  TP393  
Corresponding Authors: Lu Wei     E-mail:

Cite this article:

Yu Fengchang,Cheng Qikai,Lu Wei. Locating Academic Literature Figures and Tables with Geometric Object Clustering. Data Analysis and Knowledge Discovery, 2021, 5(1): 140-149.

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Flow Chart of the Proposed Method
A Diagram of Adding a Priori from Document
Fig. 2(e)
Schematic Diagram of Using K-means to Cluster the Objects in Fig. 2(e)
Diagram of Text Block Positioning in the Edge Area of the Figure
算法 准确率 召回率 F1
PDFFigures 2.0 0.950 0.725 0.822
本文算法 0.915 0.918 0.916
Algorithm Performance
Interference of Non-graph Geometric Elements with Localization Results
The Errors Caused by Hyphens without Using Text Symbols
Error of K Value in K-means Clustering
Diagram of Text Filtering Errors
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