Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents

• URL: http://arxiv.org/abs/2007.00779v1
• Date: Wed, 1 Jul 2020 21:48:05 GMT
• Title: Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents
• Authors: Thiago M. Paix\~ao, Rodrigo F. Berriel, Maria C. S. Boeres, Alessandro L. Koerich, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos
• Abstract summary: This work extends our previous deep learning method for single-page reconstruction to a more realistic/complex scenario. In our approach, the compatibility evaluation is modeled as a two-class (valid or invalid) pattern recognition problem. The proposed method outperforms the competing ones on complex scenarios, achieving accuracy superior to 90%.
• Score: 63.41717168981103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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%.

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