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With the advent of mobile and hand-held cameras, document images have foundtheir way into almost every domain. Dewarping of these images for the removalof perspective distortions and folds is essential so that they can beunderstood by document recognition algorithms. For this, we propose anend-to-end CNN architecture that can produce distortion free document imagesfrom warped documents it takes as input. We train this model on warped documentimages simulated synthetically to compensate for lack of enough natural data.Our method is novel in the use of a bifurcated decoder with shared weights toprevent intermingling of grid coordinates, in the use of residual networks inthe U-Net skip connections to allow flow of data from different receptivefields in the model, and in the use of a gated network to help the model focuson structure and line level detail of the document image. We evaluate ourmethod on the DocUNet dataset, a benchmark in this domain, and obtain resultscomparable to state-of-the-art methods.

RectiNet-v2: A stacked network architecture for document image dewarping