Discrete Grey Wolf Optimization for Shredded Document Reconstruction

Badawy, H. A.; Emary, Eid; Yassien, Mohamed H.; Fathi, Mahmoud

doi:10.1007/978-3-319-99010-1_26

Cited by 1 publication

(1 citation statement)

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“…Computational solutions typically involve an optimization process guided by an objective function that quantifies the global fitting (compatibility) of the shreds. This function can be computed directly from the entire image representing a reconstruction [11] or derived from the compatibility between shreds (pairwise compatibility), being the latter more common.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised deep reconstruction of mixed strip-shredded text documents

Paixão

Berriel

Bóeres

et al. 2020

Pattern Recognition

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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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Section: Introductionmentioning

confidence: 99%