Abstract:This thesis addresses the reconstruction of shredded paper documents, a relevant task in various domains such as forensic investigation and history reconstruction. Despite previous research, dealing with real-shredded data is a sensitive issue in the literature. To face this challenge, we proposed two deep learning approaches that have achieved state-of-the-art accuracy in more realistic scenarios. As a second major contribution, human interaction was explored to improve reconstruction. Our framework, inspired… Show more
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