Enhancing a Genetic Algorithm with a Solution Archive to Reconstruct Cross Cut Shredded Text Documents

Biesinger, Benjamin; Schauer, Christian; Hu, Bin; Raidl, Günther R.

doi:10.1007/978-3-642-53856-8_48

Cited by 9 publications

(9 citation statements)

References 6 publications

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“…The text document reconstruction can be categorized into different kinds of problems, including jigsaw puzzles [2,5], strip shredded documents reconstruction [6,9,11], manually torn documents reconstruction [3,4] and the cross-cut shredded documents reconstruction [1,10,13,14].…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, little progress has been made in developing the score function or the error evaluation function. In [1] Biesinger et al provided a formal definition of EEF. It selects a cost based on the weighted difference of the adjacent pixels on either side of candidate matching pairs.…”

Section: Related Workmentioning

confidence: 99%

“…Here, we propose an error evaluation function (EEF) based on gradient vector of edges. Compared with the EFF described in [10] and the cost function in [1], the EEF proposed in this work focuses on both the relationship between the pixels of edges and the diversification of the gray scales from edge to edge.…”

Section: Error Evaluation Function Based On Gradient Vectormentioning

confidence: 99%

“…Each shred has some texts and they have the same size of width and height. Let s n be the virtual shred, which is a blank piece of paper with the same size as the ones in S. It could be safely ignored because there is no information on it [1].…”

Section: Introductionmentioning

confidence: 99%

“…A solution to the RCCSTD problem can be represented by an injective mapping Π = S → D 2 , that is, each shred is mapped to a position (x, y) in the Euclidean space, where x, y ∈ D = {0, 1, 2, ..., n}. The remaining positions of the whole document are filled with many copies of virtual shreds s n [1].…”

Section: Introductionmentioning

confidence: 99%

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A Semi-automatic Solution Archive for Cross-Cut Shredded Text Documents Reconstruction

Guo

Lao

Guo

et al. 2015

Lecture Notes in Computer Science

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Automatic reconstruction of cross-cut shredded text documents (RCCSTD) is important in some areas and it is still a highly challenging problem so far. In this work, we propose a novel semi-automatic reconstruction solution archive for RCCSTD. This solution archive consists of five components, namely preprocessing, row clustering, error evaluation function (EEF), optimal reconstructing route searching and human mediation (HM). Specifically, a row clustering algorithm based on signal correlation coefficient and cross-correlation sequence, and an improved EEF based on gradient vector is separately evaluated by combining with HM and without HM. Experimental results show that row clustering is effective for identifying and grouping shreds belonging to a same row of text documents. The EEF proposed in this work improves the precision and produces high performance in RCCSTD regardless of using HM or not. Overall, extra HM boosts both of the performance of row clustering and shred reconstructing.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Error Evaluation Function Based On Gradient Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Semi-automatic Solution Archive for Cross-Cut Shredded Text Documents Reconstruction

Guo

Lao

Guo

et al. 2015

Lecture Notes in Computer Science

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Assessing Cross-Cut Shredded Document Assembly

Saboia

Goldenstein

2014

Advanced Information Systems Engineering

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Abstract. In this paper we address the problem of quantitative evaluation of cross-cut shredded document reconstruction. We propose quantitative metrics using graph theory and classic information retrieval concepts to compare the neighborhood connectivity graph of a reassembled document shredded by a cross-cut machine against the neighborhood graph of the ground-truth. These metrics focus entirely on the proper relative positioning of the shredded pieces. To do so, we have shredded 12 documents containing diverse content, such as handwriting, printed text, images and photographs. We then scanned, extracted the pieces, and reassembled them into the ground-truth. This dataset is available to the readers, with the original documents, the digital representation of the shreds, and the scripts that provide the quantitative evaluation of the users reconstructions.

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