We propose a novel learning method to rectify document images with various distortion types from a single input image. As opposed to previous learning-based methods, our approach seeks to first learn the distortion flow on input image patches rather than the entire image. We then present a robust technique to stitch the patch results into the rectified document by processing in the gradient domain. Furthermore, we propose a second network to correct the uneven illumination, further improving the readability and OCR accuracy. Due to the less complex distortion present on the smaller image patches, our patch-based approach followed by stitching and illumination correction can significantly improve the overall accuracy in both the synthetic and real datasets.
@article{arxiv.1909.09470,
title = {Document Rectification and Illumination Correction using a Patch-based CNN},
author = {Xiaoyu Li and Bo Zhang and Jing Liao and Pedro V. Sander},
journal= {arXiv preprint arXiv:1909.09470},
year = {2019}
}