Document Dewarping with Control Points (2203.10543v1)

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.

• The paper introduces a control point-based method that rectifies geometric distortions in document images to enhance OCR accuracy.
• It employs an encoder architecture to predict sparse control points, converting them into dense mappings through efficient interpolation.
• Empirical evaluations on synthetic and real data demonstrate state-of-the-art performance with improved processing speed and adaptability for mobile use.

Document Dewarping with Control Points

This paper introduces a novel strategy for rectifying geometrically distorted document images captured by handheld devices, a common challenge impeding optical character recognition (OCR) processes. The proposed method centers around the use of control points to correct distortions, offering an efficient alternative to traditional dense grid approaches prevalent in deep learning-based dewarping techniques.

Methodology Overview

The authors present a method that leverages control points to estimate geometric distortions, which are then corrected through interpolation and remapping. The key innovation lies in the use of an encoder architecture to predict these control points along with reference points, minimizing the complexity traditionally associated with dense pixel-wise regression models. By converting sparse mappings into dense backward mappings, their approach effectively rectifies document images while maintaining computational efficiency.

The flexibility of control points is a significant advantage, enabling user interaction to adjust sub-optimal vertices and facilitate customization according to different scenarios. This interactivity translates into practical benefits, such as the ability to resize images, choose the number of vertices, and even utilize the control points for semi-automated annotation of distorted documents.

Experimental Evaluation

The approach was empirically validated using a dataset of synthetic distorted document images, alongside tests conducted on real-world data from the benchmark established by Ma et al. The results indicate that the control point method achieves state-of-the-art performance, particularly in terms of global similarity metrics such as MS-SSIM, with competitive local distortion metrics. These outcomes demonstrate the method's effectiveness across various distortion types.

Moreover, the execution speed is highlighted as a crucial success factor, particularly when employing linear interpolation, which shows significant reductions in computational time without substantial degradation in performance, thereby reinforcing the method's practical applicability.

Comparisons and Implications for Future Research

The comparison made between different interpolation techniques and grid densities underscores the versatility of the control point approach, offering a fine balance between detail precision and processing speed. The potential for further advancements lies in refining the neural architecture for even lighter and more efficient models and exploring additional interpolation methods to enhance rectification quality further.

This work's implications extend into practical applications such as real-time document processing on mobile devices and enhanced OCR accuracy in diverse environments. The control points-based method also opens avenues for a varied range of applications in computer vision where geometric transformations are pivotal.

Conclusively, the paper contributes valuable insights and empirical evidence supporting the proposed methodology's efficacy in document dewarping. It also sets a platform for future research to build upon, focusing on lightweight model development, real-time applications, and broader implications for document processing technologies.