  Text line segmentation is one of the key steps in historical document
understanding. It is challenging due to the variety of fonts, contents, writing
styles and the quality of documents that have degraded through the years.
  In this paper, we address the limitations that currently prevent people from
building line segmentation models with a high generalization capacity. We
present a study conducted using three state-of-the-art systems Doc-UFCN,
dhSegment and ARU-Net and show that it is possible to build generic models
trained on a wide variety of historical document datasets that can correctly
segment diverse unseen pages. This paper also highlights the importance of the
annotations used during training: each existing dataset is annotated
differently. We present a unification of the annotations and show its positive
impact on the final text recognition results. In this end, we present a
complete evaluation strategy using standard pixel-level metrics, object-level
ones and introducing goal-oriented metrics.
