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A splicing-driven memetic algorithm for reconstructing cross-cut shredded text documents

机译：拼接驱动的模因算法重构横切文本文档

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Reconstruction of cross-cut shredded text documents (RCCSTD) plays a crucial role in many fields such as forensic and archeology. To handle and reconstruct the shreds, in addition to some image processing procedures, a well-designed optimization algorithm is required. Existing works adopt some general methods in these two aspects, which may not be very efficient since they ignore the specific structure or characteristics of RCCSTD. In this paper, we develop a splicing-driven memetic algorithm (SD-MA) specifically for tackling the problem. As the name indicates, the algorithm is designed from a splicing-centered perspective, in which the operators and fitness evaluation are developed for the purpose of splicing the shreds. We design novel crossover and mutation operators that utilize the adjacency information in the shreds to breed high-quality offsprings. Then, a local search strategy based on shreds is performed, which further improves the evolution efficiency of the population in complex search space. To extract valid information from shreds and improve the accuracy of splicing costs, we propose a comprehensive objective function that considers both edge and empty row-based splicing errors. Experiments are carried out on 30 RCCSTD scenarios and comparisons are made against previous best-known algorithms. Experimental results show that the proposed SD-MA displays a significantly improved performance in terms of solution accuracy and convergence speed. (C) 2016 Elsevier B.V. All rights reserved.
机译：横切文本文件的重建（RCCSTD）在法医学和考古学等许多领域都起着至关重要的作用。为了处理和重建碎片，除了某些图像处理程序外，还需要设计良好的优化算法。现有作品在这两个方面采用了一些通用方法，由于它们忽略了RCCSTD的特定结构或特性，因此效率可能不高。在本文中，我们开发了专门用于解决该问题的拼接驱动模因算法（SD-MA）。顾名思义，该算法是从以拼接为中心的角度设计的，该算法的开发目的是将操作员和适应性评估拼接在一起。我们设计了新颖的交叉和变异算子，它们利用切丝中的邻接信息来繁殖高质量的后代。然后，基于碎片的局部搜索策略被执行，这进一步提高了复杂搜索空间中种群的进化效率。为了从碎片中提取有效信息并提高拼接成本的准确性，我们提出了一个综合的目标函数，该函数同时考虑了基于边缘和空行的拼接错误。在30个RCCSTD场景中进行了实验，并与以前最著名的算法进行了比较。实验结果表明，提出的SD-MA在解决方案精度和收敛速度方面显示出显着改善的性能。 （C）2016 Elsevier B.V.保留所有权利。

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