  The application of handwritten text recognition to historical works is highly
dependant on accurate text line retrieval. A number of systems utilizing a
robust baseline detection paradigm have emerged recently but the advancement of
layout analysis methods for challenging scripts is held back by the lack of
well-established datasets including works in non-Latin scripts. We present a
dataset of 400 annotated document images from different domains and time
periods. A short elaboration on the particular challenges posed by handwriting
in Arabic script for layout analysis and subsequent processing steps is given.
Lastly, we propose a method based on a fully convolutional encoder-decoder
network to extract arbitrarily shaped text line images from manuscripts.
