A Method to Generate Synthetically Warped Document Image
This addresses the problem of limited benchmark data for researchers in document image processing, though it is incremental as it builds on existing warping techniques.
The paper tackles the lack of large-scale datasets for training deep learning models in document image dewarping by proposing a method to generate synthetic warped images from flat-bed scans, using warping parameters to simulate distortions, with results compared qualitatively and quantitatively to real images.
The digital camera captured document images may often be warped and distorted due to different camera angles or document surfaces. A robust technique is needed to solve this kind of distortion. The research on dewarping of the document suffers due to the limited availability of benchmark public dataset. In recent times, deep learning based approaches are used to solve the problems accurately. To train most of the deep neural networks a large number of document images is required and generating such a large volume of document images manually is difficult. In this paper, we propose a technique to generate a synthetic warped image from a flat-bedded scanned document image. It is done by calculating warping factors for each pixel position using two warping position parameters (WPP) and eight warping control parameters (WCP). These parameters can be specified as needed depending upon the desired warping. The results are compared with similar real captured images both qualitative and quantitative way.