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

Guo, Shuxuan; Lao, Songyang; Guo, Jinlin; Xiang, Hang

doi:10.1007/978-3-319-21978-3_39

Cited by 6 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address text documents, literature started to evolve from the application of pixel-level similarity metrics [3,11,17], which are fast but inaccurate, towards stroke continuity analysis [12,22] and symbol-level matching [21,34]. Strokes continuity across shreds, however, cannot be ensured since physical shredding damages the shreds' borders.…”

Section: Problem Definitionmentioning

confidence: 99%

Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning

Paixão¹,

Berriel²,

Bóeres³

et al. 2020

Preprint

View full text Add to dashboard Cite

The reconstruction of shredded documents consists in arranging the pieces of paper (shreds) in order to reassemble the original aspect of such documents. This task is particularly relevant for supporting forensic investigation as documents may contain criminal evidence. As an alternative to the laborious and time-consuming manual process, several researchers have been investigating ways to perform automatic digital reconstruction. A central problem in automatic reconstruction of shredded documents is the pairwise compatibility evaluation of the shreds, notably for binary text documents. In this context, deep learning has enabled great progress for accurate reconstructions in the domain of mechanically-shredded documents. A sensitive issue, however, is that current deep model solutions require an inference whenever a pair of shreds has to be evaluated. This work proposes a scalable deep learning approach for measuring pairwise compatibility in which the number of inferences scales linearly (rather than quadratically) with the number of shreds. Instead of predicting compatibility directly, deep models are leveraged to asymmetrically project the raw shred content onto a common metric space in which distance is proportional to the compatibility. Experimental results show that our method has accuracy comparable to the state-of-the-art with a speed-up of about 22 times for a test instance with 505 shreds (20 mixed shredded-pages from different documents).

show abstract

Section: Problem Definitionmentioning

confidence: 99%

Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning

Paixão¹,

Berriel²,

Bóeres³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Lei [11] used line information to cluster pieces from the same line. Similarly, Guo et al [12] presented a row clustering method for shreds.…”

Section: Introductionmentioning

confidence: 99%

Practical Challenge of Shredded Documents: Clustering of Chinese Homologous Pieces

et al. 2017

View full text Add to dashboard Cite

When recovering a shredded document that has numerous mixed pieces, the difficulty of the recovery process can be reduced by clustering, which is a method of grouping pieces that originally belonged to the same page. Restoring homologous shredded documents (pieces from different pages of the same file) is a frequent problem, and because these pieces have nearly indistinguishable visual characteristics, grouping them is extremely difficult. Clustering research has important practical significance for document recovery because homologous pieces are ubiquitous. Because of the wide usage of Chinese and the huge demand for Chinese shredded document recovery, our research focuses on Chinese homologous pieces. In this paper, we propose a method of completely clustering Chinese homologous pieces in which the distribution features of the characters in the pieces and the document layout are used to correlate adjacent pieces and cluster them in different areas of a document. The experimental results show that the proposed method has a good clustering effect on real pieces. For the dataset containing 10 page documents (a total of 462 pieces), its average accuracy is 97.19%.

show abstract

Clustering paper shreds of different sizes

Madain

2022

Multimed Tools Appl

View full text Add to dashboard Cite

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

Cited by 6 publications

References 12 publications

Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning

Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning

Practical Challenge of Shredded Documents: Clustering of Chinese Homologous Pieces

Clustering paper shreds of different sizes

Contact Info

Product

Resources

About