Self-supervised deep reconstruction of mixed strip-shredded text documents. (November 2020)

• Record Type: Journal Article
• Authors: Paixão, Thiago M.; Berriel, Rodrigo F.; Boeres, Maria C.S.; Koerich, Alessandro L.; Badue, Claudine; De Souza, Alberto F.; Oliveira-Santos, Thiago
• Is Part Of: Pattern recognition. Volume 107(2020:Nov.)
• Publication Date: 2020-11
• Subjects: Deep learning -- Self-supervised learning -- Fully convolutional neural networks -- Document reconstruction -- Forensics -- Optimization search; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107535
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Paper shreds matching via self-supervised deep learning. Training with simulated cuts is effective for real-shredded documents. A new public dataset with 100 strip-shredded documents (2292 shreds). Accurate (over 90% accuracy) reconstruction of 100 mixed shredded documents. Abstract: 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%.