  Automatically recognizing the layout of handwritten documents is an important
step towards useful extraction of information from those documents. The most
common application is to feed downstream applications such as automatic text
recognition and keyword spotting; however, the recognition of the layout also
helps to establish relationships between elements in the document which allows
to enrich the information that can be extracted. Most of the modern document
layout analysis systems are designed to address only one part of the document
layout problem, namely: baseline detection or region segmentation. In contrast,
we evaluate the effectiveness of the Mask-RCNN architecture to address the
problem of baseline detection and region segmentation in an integrated manner.
We present experimental results on two handwritten text datasets and one
handwritten music dataset. The analyzed architecture yields promising results,
outperforming state-of-the-art techniques in all three datasets.
