  One important and particularly challenging step in the optical character
recognition (OCR) of historical documents with complex layouts, such as
newspapers, is the separation of text from non-text content (e.g. page borders
or illustrations). This step is commonly referred to as page segmentation.
While various rule-based algorithms have been proposed, the applicability of
Deep Neural Networks (DNNs) for this task recently has gained a lot of
attention. In this paper, we perform a systematic evaluation of 11 different
published DNN backbone architectures and 9 different tiling and scaling
configurations for separating text, tables or table column lines. We also show
the influence of the number of labels and the number of training pages on the
segmentation quality, which we measure using the Matthews Correlation
Coefficient. Our results show that (depending on the task) Inception-ResNet-v2
and EfficientNet backbones work best, vertical tiling is generally preferable
to other tiling approaches, and training data that comprises 30 to 40 pages
will be sufficient most of the time.
