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Despite recent advances in the field of supervised deep learning for textline segmentation, unsupervised deep learning solutions are beginning to gainpopularity. In this paper, we present an unsupervised deep learning method thatembeds document image patches to a compact Euclidean space where distancescorrespond to a coarse text line pattern similarity. Once this space has beenproduced, text line segmentation can be easily implemented using standardtechniques with the embedded feature vectors. To train the model, we extractrandom pairs of document image patches with the assumption that neighbourpatches contain a similar coarse trend of text lines, whereas if one of them isrotated, they contain different coarse trends of text lines. Doing well on thistask requires the model to learn to recognize the text lines and their salientparts. The benefit of our approach is zero manual labelling effort. We evaluatethe method qualitatively and quantitatively on several variants of text linesegmentation datasets to demonstrate its effectivity.

Unsupervised learning of text line segmentation by differentiating coarse patterns