Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents
Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents
Paixão, Thiago M. and Berriel, Rodrigo F. and Boeres, Maria C.S. and Koerich, Alessandro L. and Badue, Claudine and de Souza, Alberto F. and Oliveira-Santos, Thiago
arXiv 2020
Abstract : 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%.
