Quantcast

A Method to Generate Synthetically Warped Document Image

Research paper by Arpan Garai, Samit Biswas, Sekhar Mandal, Bidyut. B. Chaudhuri

Indexed on: 25 Feb '21Published on: 15 Oct '19Published in: arXiv - Computer Science - Computer Vision and Pattern Recognition



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.