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Evaluation of Discriminative Models for the Reconstruction of Hand-Torn Documents

  • Fabian RichterEmail author
  • Christian X. Ries
  • Rainer Lienhart
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9005)

Abstract

This work deals with the reconstruction of hand-torn documents from pairs of aligned fragments. In the first step we use a recent approach to estimate hypotheses for aligning pieces from a set of magazine pages. We then train a structural support vector machine to determine the compatibility of previously aligned pieces along their adjacent contour regions. Based on the output of this discriminative model we induce a ranking among all pairs of pieces, as high compatibility scores often correlate with spatial configurations found in the original document. To evaluate our system’s performance we provide a new baseline on a publicly available benchmark dataset in terms of mean average precision (mAP). With the (mean) average precision being widely recognized as de facto standard for evaluation of object detection and retrieval methods, our work is devoted to establish this performance measure for document reconstruction to enable a rigorous comparison of different methods.

Keywords

Support Point Discriminative Model Outer Contour Orientation Estimate Foreground Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fabian Richter
    • 1
    Email author
  • Christian X. Ries
    • 1
  • Rainer Lienhart
    • 1
  1. 1.Multimedia Computing and Computer Vision LabUniversity of AugsburgAugsburgGermany

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