Vincent Christlein, David Bernecker, Andreas Maier, Elli Angelopoulou

Abstract. 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 mAP on the challenging bilingual ICDAR dataset.

Accidentally, we put the numbers of the training instead of the test set into Table 1b). Please see the updated table with both number sets.

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