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 The focus lies on the ICDAR17 competition dataset on historical document writer identification (Historical-WI).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-theart 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).

We present RelationFactory, a highly effective open source relation extraction system based on shallow modeling techniques. RelationFactory emphasizes modularity, is easily configurable and uses a transparent pipelined approach.The interactive demo allows the user to pose queries for which RelationFactory retrieves and analyses contexts that contain relational information about the query entity. Additionally, a recall error analysis component categorizes and illustrates cases in which the system missed a correct answer.

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