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Mathematical Variable Detection in PDF Scientific Documents

  • Bui Hai PhongEmail author
  • Thang Manh Hoang
  • Thi-Lan Le
  • Akiko Aizawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)

Abstract

The detection of mathematical expression from PDF documents has been studied and advanced for recent years. In the process, the detection of variables of inline expressions that are represented by alphabetical characters is a challenge. Compared to other components of inline expressions, there are many factors that cause the ambiguities for the detection of variables. In this paper, the error in detecting variables in PDF scientific documents is analytically presented. Novel rules are proposed to improve the accuracy in the detection process. The experimental results on benchmark datasets containing English and Vietnamese documents show the effectiveness of the proposed method. The comparison with existing methods demonstrates the out-performance of the proposed method. Furthermore, pre-trained deep Convolutional Neural Networks are employed and optimized to automatically extract visual features of extracted components from PDF and machine learning algorithms are used to improve the accuracy of the detection.

Keywords

PDF document analysis Mathematical expression extraction Machine learning Rule-based classification Deep learning 

Notes

Acknowledgement

This work was supported by JST CREST Grant Number JPMJCR1513, Japan.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bui Hai Phong
    • 1
    Email author
  • Thang Manh Hoang
    • 2
  • Thi-Lan Le
    • 1
  • Akiko Aizawa
    • 3
  1. 1.MICA International Research Institute (HUST - CNRS/UMI2954 - Grenoble INP), Hanoi University of Science and TechnologyHanoiVietnam
  2. 2.School of Electronics and TelecommunicationsHanoi University of Science and TechnologyHanoiVietnam
  3. 3.National Institute of Informatics2-1-2 Hitotsubashi Chiyoda-kuTokyo 101-8430Japan

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