  In recent years, machine learning and deep learning approaches such as
artificial neural networks have gained in popularity for the resolution of
automatic puzzle resolution problems. Indeed, these methods are able to extract
high-level representations from images, and then can be trained to separate
matching image pieces from non-matching ones. These applications have many
similarities to the problem of ancient document reconstruction from partially
recovered fragments. In this work we present a solution based on a Graph Neural
Network, using pairwise patch information to assign labels to edges
representing the spatial relationships between pairs. This network classifies
the relationship between a source and a target patch as being one of Up, Down,
Left, Right or None. By doing so for all edges, our model outputs a new graph
representing a reconstruction proposal. Finally, we show that our model is not
only able to provide correct classifications at the edge-level, but also to
generate partial or full reconstruction graphs from a set of patches.
