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

TL;DR

RectiNet-v2 introduces a novel CNN architecture for effectively dewarping document images, improving recognition accuracy by removing distortions through innovative network design and training on synthetic data.

Contribution

The paper presents a new end-to-end CNN with a bifurcated decoder, residual U-Net connections, and a gated network for improved document dewarping, trained on synthetic data.

Findings

Achieves results comparable to state-of-the-art on DocUNet dataset

Effectively removes perspective distortions from warped documents

Introduces architectural innovations for better feature flow and focus

Abstract

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