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Graph-Based Clustering for Apictorial Jigsaw Puzzles of Hand Shredded Content-less Pages

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10127)


Reassembling hand shredded content-less pages is a challenging task, with applications in forensics and fun games. This paper proposes an efficient iterative framework to solve apictorial jigsaw puzzles of hand shredded content-less pages, using only the shape information. The proposed framework consists of four phases. In the first phase, normalized shape features are extracted from fragment contours. Then, for all possible matches between pairs of fragments transformation parameters for alignment of fragments and three goodness scores are estimated. In the third phase, incorrect matches are eliminated based on the score values. The alignments are refined by pruning the set of pairwise matched fragments. Finally, a modified graph-based framework for agglomerative clustering is used to globally reassemble the page(s). Experimental evaluation of our proposed framework on an annotated dataset of shredded documents shows the efficiency in the reconstruction of multiple content-less pages from arbitrarily torn fragments.


  • Content-less page reassembly
  • Partial contour matching
  • Shape features
  • Agglomerative clustering
  • Global reassembly

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Correspondence to Lalitha K.S. .

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K.S., L., Das, S., Menon, A., Varghese, K. (2017). Graph-Based Clustering for Apictorial Jigsaw Puzzles of Hand Shredded Content-less Pages. In: Basu, A., Das, S., Horain, P., Bhattacharya, S. (eds) Intelligent Human Computer Interaction. IHCI 2016. Lecture Notes in Computer Science(), vol 10127. Springer, Cham.

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