Geometric Rectification of Creased Document Images based on Isometric Mapping

TL;DR

This paper introduces a novel geometric rectification framework for creased and folded document images using isometric mapping, significantly improving accuracy over existing methods by incorporating 3D developability and textural features.

Contribution

It proposes a general isometric mapping-based framework for rectifying complex creased documents, integrating 3D developability and textural features for enhanced accuracy.

Findings

Outperforms state-of-the-art methods in rectification accuracy.

Effectively handles complex creases and foldings in documents.

Flexible framework that can incorporate various feature extraction methods.

Abstract

Geometric rectification of images of distorted documents finds wide applications in document digitization and Optical Character Recognition (OCR). Although smoothly curved deformations have been widely investigated by many works, the most challenging distortions, e.g. complex creases and large foldings, have not been studied in particular. The performance of existing approaches, when applied to largely creased or folded documents, is far from satisfying, leaving substantial room for improvement. To tackle this task, knowledge about document rectification should be incorporated into the computation, among which the developability of 3D document models and particular textural features in the images, such as straight lines, are the most essential ones. For this purpose, we propose a general framework of document image rectification in which a computational isometric mapping model is utilized for expressing a 3D document model and its flattening in the plane. Based on this framework, both model developability and textural features are considered in the computation. The experiments and comparisons to the state-of-the-art approaches demonstrated the effectiveness and outstanding performance of the proposed method. Our method is also flexible in that the rectification results can be enhanced by any other methods that extract high-quality feature lines in the images.