  The reconstruction of shredded documents consists of coherently arranging
fragments of paper (shreds) to recover the original document(s). A great
challenge in computational reconstruction is to properly evaluate the
compatibility between the shreds. While traditional pixel-based approaches are
not robust to real shredding, more sophisticated solutions compromise
significantly time performance. The solution presented in this work extends our
previous deep learning method for single-page reconstruction to a more
realistic/complex scenario: the reconstruction of several mixed shredded
documents at once. In our approach, the compatibility evaluation is modeled as
a two-class (valid or invalid) pattern recognition problem. The model is
trained in a self-supervised manner on samples extracted from
simulated-shredded documents, which obviates manual annotation. Experimental
results on three datasets -- including a new collection of 100 strip-shredded
documents produced for this work -- have shown that the proposed method
outperforms the competing ones on complex scenarios, achieving accuracy
superior to 90%.
