  Extraction of text regions and individual text lines from historic documents
is necessary for automatic transcription. We propose extending a CNN-based text
baseline detection system by adding line height and text block boundary
predictions to the model output, allowing the system to extract more
comprehensive layout information. We also show that pixel-wise text orientation
prediction can be used for processing documents with multiple text
orientations. We demonstrate that the proposed method performs well on the cBAD
baseline detection dataset. Additionally, we benchmark the method on newly
introduced PERO layout dataset which we also make public.
