  This paper introduces a new way for text-line extraction by integrating
deep-learning based pre-classification and state-of-the-art segmentation
methods. Text-line extraction in complex handwritten documents poses a
significant challenge, even to the most modern computer vision algorithms.
Historical manuscripts are a particularly hard class of documents as they
present several forms of noise, such as degradation, bleed-through, interlinear
glosses, and elaborated scripts. In this work, we propose a novel method which
uses semantic segmentation at pixel level as intermediate task, followed by a
text-line extraction step. We measured the performance of our method on a
recent dataset of challenging medieval manuscripts and surpassed
state-of-the-art results by reducing the error by 80.7%. Furthermore, we
demonstrate the effectiveness of our approach on various other datasets written
in different scripts. Hence, our contribution is two-fold. First, we
demonstrate that semantic pixel segmentation can be used as strong denoising
pre-processing step before performing text line extraction. Second, we
introduce a novel, simple and robust algorithm that leverages the high-quality
semantic segmentation to achieve a text-line extraction performance of 99.42%
line IU on a challenging dataset.
