  We present an unsupervised deep learning method for text line segmentation
that is inspired by the relative variance between text lines and spaces among
text lines. Handwritten text line segmentation is important for the efficiency
of further processing. A common method is to train a deep learning network for
embedding the document image into an image of blob lines that are tracing the
text lines. Previous methods learned such embedding in a supervised manner,
requiring the annotation of many document images. This paper presents an
unsupervised embedding of document image patches without a need for
annotations. The number of foreground pixels over the text lines is relatively
different from the number of foreground pixels over the spaces among text
lines. Generating similar and different pairs relying on this principle
definitely leads to outliers. However, as the results show, the outliers do not
harm the convergence and the network learns to discriminate the text lines from
the spaces between text lines. Remarkably, with a challenging Arabic
handwritten text line segmentation dataset, VML-AHTE, we achieved superior
performance over the supervised methods. Additionally, the proposed method was
evaluated on the ICDAR 2017 and ICFHR 2010 handwritten text line segmentation
datasets.
