  We present docExtractor, a generic approach for extracting visual elements
such as text lines or illustrations from historical documents without requiring
any real data annotation. We demonstrate it provides high-quality performances
as an off-the-shelf system across a wide variety of datasets and leads to
results on par with state-of-the-art when fine-tuned. We argue that the
performance obtained without fine-tuning on a specific dataset is critical for
applications, in particular in digital humanities, and that the line-level page
segmentation we address is the most relevant for a general purpose element
extraction engine. We rely on a fast generator of rich synthetic documents and
design a fully convolutional network, which we show to generalize better than a
detection-based approach. Furthermore, we introduce a new public dataset dubbed
IlluHisDoc dedicated to the fine evaluation of illustration segmentation in
historical documents.
