ShreddingNet: Coarse-to-Fine Restoration for Multi-Source Shredded Manuscripts

As an important research task of human cultural heritage, the restoration of artworks and calligraphy is of great significance.Seldom existing works have taken the multi-source (i.e., fragments are not ensured to be from the same piece of artworks) fragment-oriented restoration task into account.We propose ShreddingNet, a coarse-to-fine two-stage pipeline for multi-source manuscript restoration that operates without restrictive conditions.The proposed coarse stage compares the features of each fragment, selecting top-K candidates and clustering fragments by source. This design leverages the key insight that erroneous matches rarely cross source boundaries, enabling high-precision clustering.The proposed fine-grained stage evaluates candidates, yielding matching scores and filters out erroneous matching pairs from the candidate set; producing more precise final matching pairs for global assembly. Experiments conducted on more than 4,000 images from two datasets demonstrate the average reconstruction F1-score achieves 98.37\%, which is 5.72\% higher than the current state-of-the-art method, confirming the method’s effectiveness and robustness.Source code is available in the supplementary material.