  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.
