Geometry Restoration and Dewarping of Camera-Captured Document Images

• URL: http://arxiv.org/abs/2501.03145v2
• Date: Thu, 09 Jan 2025 15:31:29 GMT
• Title: Geometry Restoration and Dewarping of Camera-Captured Document Images
• Authors: Valery Istomin, Oleg Pereziabov, Ilya Afanasyev,
• Abstract summary: This research focuses on developing a method for restoring the topology of digital images of paper documents captured by a camera. Our methodology employs deep learning (DL) for document outline detection, followed by computer vision (CV) to create a topological 2D grid.
• Score: 0.0
• License:
• Abstract: This research focuses on developing a method for restoring the topology of digital images of paper documents captured by a camera, using algorithms for detection, segmentation, geometry restoration, and dewarping. Our methodology employs deep learning (DL) for document outline detection, followed by computer vision (CV) to create a topological 2D grid using cubic polynomial interpolation and correct nonlinear distortions by remapping the image. Using classical CV methods makes the document topology restoration process more efficient and faster, as it requires significantly fewer computational resources and memory. We developed a new pipeline for automatic document dewarping and reconstruction, along with a framework and annotated dataset to demonstrate its efficiency. Our experiments confirm the promise of our methodology and its superiority over existing benchmarks (including mobile apps and popular DL solutions, such as RectiNet, DocGeoNet, and DocTr++) both visually and in terms of document readability via Optical Character Recognition (OCR) and geometry restoration metrics. This paves the way for creating high-quality digital copies of paper documents and enhancing the efficiency of OCR systems. Project page: https://github.com/HorizonParadox/DRCCBI

Related papers

• Optimization of Image Processing Algorithms for Character Recognition in Cultural Typewritten Documents [0.8158530638728501]
  This paper evaluates the impact of image processing methods and parameter tuning in Optical Character Recognition (OCR) The approach uses a multi-objective problem formulation to minimize Levenshtein edit distance and maximize the number of words correctly identified with a non-dominated sorting genetic algorithm (NSGA-II) Our findings suggest that employing image pre-processing algorithms in OCR might be more suitable for typologies where the text recognition task without pre-processing does not produce good results.
  arXiv  Detail & Related papers  (2023-11-27T11:44:46Z)
• DocMAE: Document Image Rectification via Self-supervised Representation Learning [144.44748607192147]
  We present DocMAE, a novel self-supervised framework for document image rectification. We first mask random patches of the background-excluded document images and then reconstruct the missing pixels. With such a self-supervised learning approach, the network is encouraged to learn the intrinsic structure of deformed documents.
  arXiv  Detail & Related papers  (2023-04-20T14:27:15Z)
• Deep Unrestricted Document Image Rectification [110.61517455253308]
  We present DocTr++, a novel unified framework for document image rectification. We upgrade the original architecture by adopting a hierarchical encoder-decoder structure for multi-scale representation extraction and parsing. We contribute a real-world test set and metrics applicable for evaluating the rectification quality.
  arXiv  Detail & Related papers  (2023-04-18T08:00:54Z)
• Geometric Representation Learning for Document Image Rectification [137.75133384124976]
  We present DocGeoNet for document image rectification by introducing explicit geometric representation. Our motivation arises from the insight that 3D shape provides global unwarping cues for rectifying a distorted document image. Experiments show the effectiveness of our framework and demonstrate the superiority of our framework over state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-10-15T01:57:40Z)
• Fourier Document Restoration for Robust Document Dewarping and Recognition [73.44057202891011]
  This paper presents FDRNet, a Fourier Document Restoration Network that can restore documents with different distortions. It dewarps documents by a flexible Thin-Plate Spline transformation which can handle various deformations effectively without requiring deformation annotations in training. It outperforms the state-of-the-art by large margins on both dewarping and text recognition tasks.
  arXiv  Detail & Related papers  (2022-03-18T12:39:31Z)
• DocScanner: Robust Document Image Rectification with Progressive Learning [162.03694280524084]
  This work presents DocScanner, a new deep network architecture for document image rectification. DocScanner maintains a single estimate of the rectified image, which is progressively corrected with a recurrent architecture. The iterative refinements make DocScanner converge to a robust and superior performance, and the lightweight recurrent architecture ensures the running efficiency.
  arXiv  Detail & Related papers  (2021-10-28T09:15:02Z)
• DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction [99.09177377916369]
  We propose Document Image Transformer (DocTr) to address the issue of geometry and illumination distortion of the document images. Our DocTr achieves 20.02% Character Error Rate (CER), a 15% absolute improvement over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-25T13:27:10Z)
• RectiNet-v2: A stacked network architecture for document image dewarping [16.249023269158734]
  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. We evaluate our method on the DocUNet dataset, a benchmark in this domain, and obtain results comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-01T19:26:17Z)
• Can You Read Me Now? Content Aware Rectification using Angle Supervision [14.095728009592763]
  We present CREASE: Content Aware Rectification using Angle Supervision, the first learned method for document rectification. Our method surpasses previous approaches in terms of OCR accuracy, geometric error and visual similarity.
  arXiv  Detail & Related papers  (2020-08-05T16:58:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.