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Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation

Luan Pham, Phu Hao Hoang, Xuan Toan Mai, Tuan Anh Tran

TL;DR

A novel skew estimation method is proposed that extracts the dominant skew angle of the given document image by applying an Adaptive Radial Projection on the 2D Discrete Fourier Magnitude spectrum.

Abstract

Skew estimation is one of the vital tasks in document processing systems, especially for scanned document images, because its performance impacts subsequent steps directly. Over the years, an enormous number of researches focus on this challenging problem in the rise of digitization age. In this research, we first propose a novel skew estimation method that extracts the dominant skew angle of the given document image by applying an Adaptive Radial Projection on the 2D Discrete Fourier Magnitude spectrum. Second, we introduce a high quality skew estimation dataset DISE-2021 to assess the performance of different estimators. Finally, we provide comprehensive analyses that focus on multiple improvement aspects of Fourier-based methods. Our results show that the proposed method is robust, reliable, and outperforms all compared methods. The source code is available at https://github.com/phamquiluan/jdeskew.

Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation

TL;DR

A novel skew estimation method is proposed that extracts the dominant skew angle of the given document image by applying an Adaptive Radial Projection on the 2D Discrete Fourier Magnitude spectrum.

Abstract

Skew estimation is one of the vital tasks in document processing systems, especially for scanned document images, because its performance impacts subsequent steps directly. Over the years, an enormous number of researches focus on this challenging problem in the rise of digitization age. In this research, we first propose a novel skew estimation method that extracts the dominant skew angle of the given document image by applying an Adaptive Radial Projection on the 2D Discrete Fourier Magnitude spectrum. Second, we introduce a high quality skew estimation dataset DISE-2021 to assess the performance of different estimators. Finally, we provide comprehensive analyses that focus on multiple improvement aspects of Fourier-based methods. Our results show that the proposed method is robust, reliable, and outperforms all compared methods. The source code is available at https://github.com/phamquiluan/jdeskew.
Paper Structure (16 sections, 4 equations, 4 figures, 4 tables)

This paper contains 16 sections, 4 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Proposed Method
  3. Overview
  4. Adaptive Radial Projection
  5. Data
  6. Verification Mask
  7. DISE 2021
  8. Experiment
  9. Criteria
  10. Threshold Configurations
  11. Analysis of the impact of different factors
  12. Image division
  13. Power spectrum and magnitude spectrum
  14. Discarding the DC component and small frequencies
  15. Experimental Results
  16. ...and 1 more sections

Figures (4)

  • Figure 1: The overview of our adaptive projection.
  • Figure 2: The change of AED and CE levels when tuning the distance $\mathcal{D}$. The chosen threshold (x) should bring the lowest AED value.
  • Figure 3: An example of low-quality annotation in DISEC2013, deskewed by DISEC2013 ground-truth angle (left) and our system (right).
  • Figure 4: (a) The sorted absolute error curve and (b) its log-scale version of four different methods on DISE 2021 ($15^{\circ}$) dataset.