Combining Low-Level Features of Offline Questionnaires for Handwriting Identification

  • Dirk Siegmund
  • Tina Ebert
  • Naser Damer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9730)


When using anonymous offline questionnaires for reviewing services or products it is often not guaranteed that a reviewer does this only once as intended. In this paper an applied combination of different features of handwritten characteristics and its fusion is presented to expose such manipulations. The presented approach covers the aspects of alignment normalization, segmentation, feature extraction, classification and fusion. Nine features from handwritten text, numbers and checkboxes are extracted and used to recognize hand-writer duplicates. The proposed method has been tested on a novel database containing pages of handwritten text produced by 1,734 writers. Furthermore we show that the unified biometric decision using a weighted sum combination rule can significantly improve writer identification performance even on low level features.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Fraunhofer Institute for Computer Graphics Research (IGD)DarmstadtGermany

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