Footnote-Based Document Image Classification

  • Sara ZhalehpourEmail author
  • Andrew Piper
  • Chad Wellmon
  • Mohamed Cheriet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10317)


Analyzing historical document images is considered a challenging task due to the complex and unusual structures of these images. It is even more challenging to automatically find the footnotes in them. In fact, detecting footnotes is one of the essential elements for scholars to analyze and answer key questions in the historical documents. In this work, we present a new framework for footnote detection in historical documents. To this aim, we used the most salient feature of the footnotes, which is their smaller font size compared to the rest of the page content. We proposed three types of features to track the font size changes and fed them to two classifiers: SVM and AdaBoost. The framework shows promising results over 80% for both classifiers using our dataset.


Visual information retrieval Footnote detection Historical documents classification 



This publication was made possible by a grant from SSHRC Canada for “The Visibility of Knowledge” project. I would also like to express my gratitude to Ehsan Arabnejad for his detailed and valuable comments.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sara Zhalehpour
    • 1
    Email author
  • Andrew Piper
    • 2
  • Chad Wellmon
    • 3
  • Mohamed Cheriet
    • 1
  1. 1.École de Technologie SupérieureUniversity of QuebecMontrealCanada
  2. 2.McGill UniversityMontrealCanada
  3. 3.University of VirginiaCharlottesvilleUSA

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