  Deep Convolutional Neural Networks (CNN) have shown great success in
supervised classification tasks such as character classification or dating.
Deep learning methods typically need a lot of annotated training data, which is
not available in many scenarios. In these cases, traditional methods are often
better than or equivalent to deep learning methods. In this paper, we propose a
simple, yet effective, way to learn CNN activation features in an unsupervised
manner. Therefore, we train a deep residual network using surrogate classes.
The surrogate classes are created by clustering the training dataset, where
each cluster index represents one surrogate class. The activations from the
penultimate CNN layer serve as features for subsequent classification tasks. We
evaluate the feature representations on two publicly available datasets. The
focus lies on the ICDAR17 competition dataset on historical document writer
identification (Historical-WI). We show that the activation features trained
without supervision are superior to descriptors of state-of-the-art writer
identification methods. Additionally, we achieve comparable results in the case
of handwriting classification using the ICFHR16 competition dataset on
historical Latin script types (CLaMM16).
