  Binary image based classification and retrieval of documents of an
intellectual nature is a very challenging problem. Variations in the binary
image generation mechanisms which are subject to the document artisan designer
including drawing style, view-point, inclusion of multiple image components are
plausible causes for increasing the complexity of the problem. In this work, we
propose a method suitable to binary images which bridges some of the successes
of deep learning (DL) to alleviate the problems introduced by the
aforementioned variations. The method consists on extracting the shape of
interest from the binary image and applying a non-Euclidean geometric
neural-net architecture to learn the local and global spatial relationships of
the shape. Empirical results show that our method is in some sense invariant to
the image generation mechanism variations and achieves results outperforming
existing methods in a patent image dataset benchmark.
