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Clustering paper shreds of different sizes

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Abstract

Although paper shredding is widely used to prevent confidential papers from being misused, still it cannot be considered a convenient process. The size of paper shreds became smaller and smaller, as new methods of shredded paper reassembly and reconstruction are evolving. This paper focuses on clustering, which is a possible phase in the assembly process. This work considers real strip-cut shreds, in addition to images shredded by a simulator in one direction to make strip-cut shreds of different sizes, from wide to narrow shreds, and images shredded in two directions, possibly reflecting cross-cut and micro-cut shreds. K-means is used to cluster shreds, the features tested are gray-level ranges, and the well-known gray-level co-occurrence matrix, invariant moments, segmentation-based fractal texture analysis algorithm, and color moments. The number of shreds grouped in the same cluster with originally adjacent neighbors is used to indicate clustering effectiveness, in addition to the overall accuracy of strip-cut shreds clustering. When the number of clusters is 5, and the k-means experiments run 100 times for 38 images, the overall accuracy of gray-level ranges in simulated strip-cut shreds is 84.87, 89.27, and 93.5 percent in the three different sizes tested, also in cross-cut and micro-cut shreds, gray-level ranges achieve a relatively high number of shreds with 3 and 4 originally adjacent neighbors found in the same cluster.

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Acknowledgements

The author would like to thank the anonymous reviewers for their valuable comments.

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  1. The Department of Computer Science, Jordan University of Science and Technology, Irbid, 22110, Jordan

    Alia Madain

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Madain, A. Clustering paper shreds of different sizes. Multimed Tools Appl 82, 19441–19461 (2023). https://doi.org/10.1007/s11042-022-13835-7

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