Efficient Algorithms for Destroyed Document Reconstruction
Your problem is NP-Complete, even for strips (n strips yields (2n)!)  used, so people use heuristics, transforms like Hough and morphological filters (to match continuity of text, but this heavily increases complexity for matching) or any kind of genetic / NN search, Ants Colony Optimization.  

For summary of consecutive steps and various algorithm I recommend An Investigation into Automated Shredded Document Reconstruction using Heuristic Search Algorithms.

The problem itself may end up in nasty cases, when document is not fully sharp (blurred, printed with low resolution) and strips width is small and cut by physical cutter with dulled edges, because standard merging methods like panorama photo sticher gets lost and yield improper results. This is due to lost information by missing small strips, otherwise if you have full digital image cut into pieces, it is as hard as Jigsaw puzzle, non-digital image falls into approximate search.

To make algorithm automatic another problem is pieces feeding, rarely you can give axis aligned strips, so to start process it is nice to input all stripes as one picture with pieces lay by hand, this imposes another problem (this one is easy) to detect blobs and rotate them.

By special shredder instead of stripes yield very small rectangles. For comparison, P-1 class shredder gives stripes 6-12mm wide of any length (about 1800mm^2), class P-7 gives rectangles with area less than 5mm^2. When you get rectangles instead of stripes, problem yields (4n)! permutations, assuming one one-sided document, if there are lots of shreds from unrelated documents (no pictures, text only) in one bag, problem is not really tractable.