A Method to Generate Synthetically Warped Document Image

Abstract

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.

Generation of realistic synthetic cable images to train deep learning segmentation models

“…It is worth noting that, in some applications, warping augmentation techniques are not limited to incrementing the samples of a dataset, but they can be utilized to create entire synthetic datasets for learning how to reverse the applied transformations. This approach was demonstrated by Garai et al in [30] and [31] learning how to dewarp warped images. Thus, a synthetic dataset of warped images was created by applying geometrical transformations to real flat-bed scanned images using a mathematical model.…”