As CNN architecture, we employ a DenseNet-121 (Huang et al. 2017). It is composed of 121 neural layers, most of them contained in 4 densely connected blocks. To identify the main font in a document image, we split it into many overlapping 224x224 px large patches, which are subsequently passed to the CNN. The overall page result is obtained by taking the average of all classified patches. Processing pages patch-wise is significantly more memory-friendly than using fully-convolutional neural networks and does not require expensive hardware.