  Text line segmentation is one of the pre-stages of modern optical character
recognition systems. The algorithmic approach proposed by this paper has been
designed for this exact purpose. Its main characteristic is the combination of
two different techniques, morphological image operations and horizontal
histogram projections. The method was developed to be applied on a historic
data collection that commonly features quality issues, such as degraded paper,
blurred text, or curved text lines. For that reason, the segmenter in question
could be of particular interest for cultural institutions, such as libraries,
archives, museums, ..., that want access to robust line bounding boxes for a
given historic document. Because of the promising segmentation results that are
joined by low computational cost, the algorithm was incorporated into the OCR
pipeline of the National Library of Luxembourg, in the context of the
initiative of reprocessing their historic newspaper collection. The general
contribution of this paper is to outline the approach and to evaluate the gains
in terms of accuracy and speed, comparing it to the segmentation algorithm
bundled with the used open source OCR software.
