Currently, this process is much too slow and too labor-intensive, particularly if the documents are handwritten. We are looking to the Shredder Challenge to generate some leap-ahead thinking in this area.

Such systems humans are much more efficient at abstracting and matching take advantage of human abilities--particularly in vision, visual cues across piece borders based on their content. For natural language, and pattern recognition--to handle example, a person looking at a piece of a shredded document instances and aspects of problems that are difficult for can recognize a letter that is only partially present, and an computers. The ESP game (von Ahn and Dabbish 2008), experienced archaeologist looking at a particular piece of FoldIt (Cooper et al. 2010), and reCAPTCHA (von Ahn et a broken artifact can recognize unique patterns that extend al. 2008) are a few examples of successful systems that draw beyond the fragment. Unfortunately, for a human to find a on human contributors and machine computations to tackle matching piece still requires scanning through the pieces, problems in image labeling, protein folding, and text digitization.