用自适应径向投影提升文档图像倾斜角估计精度
Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation
- 在二维离散傅里叶幅值谱上应用自适应径向投影提取主倾斜角
- 在DISE-2021数据集上优于所有对比方法，结果更鲁棒可靠
- 适用于扫描文档处理系统，尤其适合需要高精度倾斜校正场景
倾斜角估计是文档处理系统中的关键任务，尤其对扫描文档图像影响显著。本文提出一种新型倾斜角估计方法，通过在二维离散傅里叶幅值谱上施加自适应径向投影，提取图像主倾斜角。同时构建高质量评估数据集DISE-2021，用于测试不同估计算法性能。通过全面分析，验证了该方法在多种改进方向上的有效性。实验结果表明，所提方法具有优异的鲁棒性与可靠性，显著优于现有方法。源代码已开源：https://github.com/phamquiluan/jdeskew。
原文摘要 · Abstract (English)
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.
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