Document Dewarping with Control Points

TL;DR

This paper introduces a control point-based method for rectifying distorted document images captured by mobile devices, improving OCR accuracy by effectively correcting geometric distortions with flexible and interactive adjustments.

Contribution

The paper presents a novel control point estimation and interpolation approach for document dewarping, along with a new dataset for training and evaluation.

Findings

Achieves state-of-the-art dewarping performance on real-world datasets.

Flexible control points allow for interactive adjustments and scenario-specific customization.

Provides a new dataset and code for the community to facilitate further research.

Abstract

Document images are now widely captured by handheld devices such as mobile phones. The OCR performance on these images are largely affected due to geometric distortion of the document paper, diverse camera positions and complex backgrounds. In this paper, we propose a simple yet effective approach to rectify distorted document image by estimating control points and reference points. After that, we use interpolation method between control points and reference points to convert sparse mappings to backward mapping, and remap the original distorted document image to the rectified image. Furthermore, control points are controllable to facilitate interaction or subsequent adjustment. We can flexibly select post-processing methods and the number of vertices according to different application scenarios. Experiments show that our approach can rectify document images with various distortion types, and yield state-of-the-art performance on real-world dataset. This paper also provides a training dataset based on control points for document dewarping. Both the code and the dataset are released at https://github.com/gwxie/Document-Dewarping-with-Control-Points.