  Segmenting handwritten document images into regions with homogeneous patterns
is an important pre-processing step for many document images analysis tasks.
Hand-labeling data to train a deep learning model for layout analysis requires
significant human effort. In this paper, we present an unsupervised deep
learning method for page segmentation, which revokes the need for annotated
images. A siamese neural network is trained to differentiate between patches
using their measurable properties such as number of foreground pixels, and
average component height and width. The network is trained that spatially
nearby patches are similar. The network's learned features are used for page
segmentation, where patches are classified as main and side text based on the
extracted features. We tested the method on a dataset of handwritten document
images with quite complex layouts. Our experiments show that the proposed
unsupervised method is as effective as typical supervised methods.
