Android de-Shredder App

Sensitive documents are usually shredded into strips before discarding them. Shredders are used to cut the pages of a document into thin strips of uniform thickness. Each shredded piece in the collection bin could belong to any of the pages in a document. The task of document reconstruction involves two steps: Identifying the page to which each shred belongs and rearranging the shreds within the page to their original position. The difficulty of the reconstruction process depends on the thickness of the shred and type of cut (horizontal or vertical). The thickness of the shred is directly proportional to the ease of reconstruction. Horizontal cuts are easier to reconstruct because sentences in a page are intact and not broken. Vertical cuts are harder because there is very little information to glean from each shred. In this project, an Android app is developed to reconstruct the pages of a shredded document by using a photograph of the shreds as input. However, no prior knowledge of the page to which each shred belongs is assumed. The thickness of each shred should conform to the measurements of a standard strip shredder. The type of shredder cut is vertical. This work is an enhancement of an existing work of puzzle reconstruction developed by Hammoudeh and Pollett. Through the experiments conducted on both the existing model and the proposed model, it was found that the proposed pixel correlation metric model performed with 80 to 90% better accuracy than the existing RGB metric model on grayscale document images. However, the performance on high contrast images remained almost the same at 90% accuracy for both the RGB model and pixel correlation metric model

L'apprentissage profond pour le réassemblage d'images patrimoniales

International audienceDans cet article, nous présentons Deepzzle, une méthode de résolution de puzzles reposant sur l'apprentissage automatique, adaptée au réassemblage d'objets patrimoniaux. En effet, ceux-ci subissent des contraintes particulières : érosion, fragments manquants, morceaux mélangés entre objets, etc. Deepzzle apporte une solution à ces problèmes. Abstract-In this paper, we present Deepzzle, a puzzle-solving method based on deep learning. Deepzzle is able to handle efficiently cultural heritage constraints: heritage collections suffer from erosion, missing fragments and mixed puzzles

Solving Jigsaw Puzzles with Eroded Boundaries

Jigsaw puzzle solving is an intriguing problem which has been explored in computer vision for decades. This paper focuses on a specific variant of the problem - solving puzzles with eroded boundaries. Such erosion makes the problem extremely difficult, since most existing solvers utilize solely the information at the boundaries. Nevertheless, this variant is important since erosion and missing data often occur at the boundaries. The key idea of our proposed approach is to inpaint the eroded boundaries between puzzle pieces and later leverage the quality of the inpainted area to classify a pair of pieces as 'neighbors or not'. An interesting feature of our architecture is that the same GAN discriminator is used for both inpainting and classification; Training of the second task is simply a continuation of the training of the first, beginning from the point it left off. We show that our approach outperforms other SOTA methodsComment: 8 page

A Novel Hybrid Scheme Using Genetic Algorithms and Deep Learning for the Reconstruction of Portuguese Tile Panels

This paper presents a novel scheme, based on a unique combination of genetic algorithms (GAs) and deep learning (DL), for the automatic reconstruction of Portuguese tile panels, a challenging real-world variant of the jigsaw puzzle problem (JPP) with important national heritage implications. Specifically, we introduce an enhanced GA-based puzzle solver, whose integration with a novel DL-based compatibility measure (DLCM) yields state-of-the-art performance, regarding the above application. Current compatibility measures consider typically (the chromatic information of) edge pixels (between adjacent tiles), and help achieve high accuracy for the synthetic JPP variant. However, such measures exhibit rather poor performance when applied to the Portuguese tile panels, which are susceptible to various real-world effects, e.g., monochromatic panels, non-squared tiles, edge degradation, etc. To overcome such difficulties, we have developed a novel DLCM to extract high-level texture/color statistics from the entire tile information. Integrating this measure with our enhanced GA-based puzzle solver, we have demonstrated, for the first time, how to deal most effectively with large-scale real-world problems, such as the Portuguese tile problem. Specifically, we have achieved 82% accuracy for the reconstruction of Portuguese tile panels with unknown piece rotation and puzzle dimension (compared to merely 3.5% average accuracy achieved by the best method known for solving this problem variant). The proposed method outperforms even human experts in several cases, correcting their mistakes in the manual tile assembly

Solving Jigsaw Puzzles By the Graph Connection Laplacian

We propose a novel mathematical framework to address the problem of automatically solving large jigsaw puzzles. This problem assumes a large image, which is cut into equal square pieces that are arbitrarily rotated and shuffled, and asks to recover the original image given the transformed pieces. The main contribution of this work is a method for recovering the rotations of the pieces when both shuffles and rotations are unknown. A major challenge of this procedure is estimating the graph connection Laplacian without the knowledge of shuffles. We guarantee some robustness of the latter estimate to measurement errors. A careful combination of our proposed method for estimating rotations with any existing method for estimating shuffles results in a practical solution for the jigsaw puzzle problem. Numerical experiments demonstrate the competitive accuracy of this solution, its robustness to corruption and its computational advantage for large puzzles