Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents
| Autor: | Paixão, Thiago M., Berriel, Rodrigo F., Boeres, Maria C. S., Koerich, Alessandro L., Badue, Claudine, de Souza, Alberto F., Oliveira-Santos, Thiago |
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| Rok vydání: | 2020 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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%. Comment: Accepted for publication in Pattern Recognition |
| Databáze: | arXiv |
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