Papers › Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation
Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation
Luan Pham; Phu Hao Hoang; Xuan Toan Mai; Tuan Anh Tran
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. Data and code are available at https://github.com/phamquiluan/jdeskew.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Document Image Skew Estimation | DISE 2021 Dataset | JDeskew | Percentage correct | 0.86 | #1 of 2 | Archive leaderboard | report |
| Document Image Skew Estimation | DISE 2021 Dataset | PypiDeskew | Percentage correct | 0.2 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: JDeskew
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