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

• URL: http://arxiv.org/abs/2003.10063v4
• Date: Wed, 29 Apr 2020 00:21:31 GMT
• Title: Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning
• Authors: Thiago M. Paix\~ao, Rodrigo F. Berriel, Maria C. S. Boeres, Alessando L. Koerich, Claudine Badue, Alberto F. De Souza and Thiago Oliveira-Santos
• Abstract summary: A central problem in automatic reconstruction of shredded documents is the pairwise compatibility evaluation of the shreds. This work proposes a scalable deep learning approach for measuring pairwise compatibility in which the number of inferences scales linearly. 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.
• Score: 62.34197797857823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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