Deep Learning Networks for Off-Line Handwritten Signature Recognition

  • Bernardete Ribeiro
  • Ivo Gonçalves
  • Sérgio Santos
  • Alexander Kovacec
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


Reliable identification and verification of off-line handwritten signatures from images is a difficult problem with many practical applications. This task is a difficult vision problem within the field of biometrics because a signature may change depending on psychological factors of the individual. Motivated by advances in brain science which describe how objects are represented in the visual cortex, advanced research on deep neural networks has been shown to work reliably on large image data sets. In this paper, we present a deep learning model for off-line handwritten signature recognition which is able to extract high-level representations. We also propose a two-step hybrid model for signature identification and verification improving the misclassification rate in the well-known GPDS database.


Deep Learning Generative Models Signature Recognition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bernardete Ribeiro
    • 1
  • Ivo Gonçalves
    • 1
  • Sérgio Santos
    • 1
  • Alexander Kovacec
    • 2
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraPortugal
  2. 2.Department of MathematicsUniversity of CoimbraPortugal

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