  Despite recent advances in the field of supervised deep learning for text
line segmentation, unsupervised deep learning solutions are beginning to gain
popularity. In this paper, we present an unsupervised deep learning method that
embeds document image patches to a compact Euclidean space where distances
correspond to a coarse text line pattern similarity. Once this space has been
produced, text line segmentation can be easily implemented using standard
techniques with the embedded feature vectors. To train the model, we extract
random pairs of document image patches with the assumption that neighbour
patches contain a similar coarse trend of text lines, whereas if one of them is
rotated, they contain different coarse trends of text lines. Doing well on this
task requires the model to learn to recognize the text lines and their salient
parts. The benefit of our approach is zero manual labelling effort. We evaluate
the method qualitatively and quantitatively on several variants of text line
segmentation datasets to demonstrate its effectivity.
