Offline Writer Identification Using Convolutional Neural Network Activation Features

  • Vincent Christlein
  • David Bernecker
  • Andreas Maier
  • Elli Angelopoulou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)


Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR 2013 benchmark database and the CVL dataset. While we perform comparably to the state of the art on CVL, our proposed method yields about 0.21 absolute improvement in terms of \(\mathrm {mAP}\) on the challenging bilingual ICDAR dataset.



This work has been supported by the German Federal Ministry of Education and Research (BMBF), grant-nr. 01UG1236a. The contents of this publication are the sole responsibility of the authors.


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© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Vincent Christlein
    • 1
  • David Bernecker
    • 1
  • Andreas Maier
    • 1
  • Elli Angelopoulou
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany

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