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摘要
En 中文
In this paper, we address the problem of automatically assembling shredded documents. We propose a two-step algorithmic framework. First, we digitize each fragment of a given document and extract shape- and content-based local features. Based on these multimodal features, we identify pairs of corresponding points on all pairs of fragments using an SVM classifier. Each pair is considered a point of attachment for aligning the respective fragments. In order to restore the layout of the document, we create a document graph in which nodes represent fragments and edges correspond to alignments. We assign weights to the edges by evaluating the alignments using a set of inter-fragment constraints which take into account shape- and content-based information. Finally, we use an iterative algorithm that chooses the edge having the highest weight during each iteration. However, since selecting edges corresponds to combining groups of fragments and thus provides new evidence, we reevaluate the edge weights after each iteration. We quantitatively evaluate the effectiveness of our approach by conducting experiments on a novel dataset. It comprises a total of 120 pages taken from two magazines which have been shredded and annotated manually. We thus provide the means for a quantitative evaluation of assembly algorithms which, to the best of our knowledge, has not been done before.
Keyword:
Annotated dataset
document assembly
graph algorithm
supervised learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.7
论文数:
4.5K
被引数:
2.4W
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