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Related Concept Videos

Bootstrapping01:24

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
Skewness01:06

Skewness

The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency are...
Types of Skewness01:09

Types of Skewness

If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...

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Related Experiment Video

Updated: Jun 15, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Skew estimation of document images using bagging.

Gaofeng Meng1, Chunhong Pan, Nanning Zheng

  • 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. gfmeng@nlpr.ia.ac.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 19, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method to estimate document image skew angles using local visual cues. It improves accuracy and speed, outperforming existing techniques for various challenging document types.

Related Experiment Videos

Last Updated: Jun 15, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Area of Science:

  • Computer Vision
  • Image Processing
  • Document Analysis

Background:

  • Accurate skew angle estimation is crucial for document image analysis.
  • Existing methods often rely solely on text lines, limiting their robustness.
  • Handling diverse document types with varying skew presents a significant challenge.

Purpose of the Study:

  • To propose a general-purpose method for estimating document image skew angles.
  • To improve the accuracy and robustness of skew angle estimation.
  • To develop a method that is efficient and effective across various document conditions.

Main Methods:

  • Exploiting local visual cues for skew angle estimation, moving beyond text-line-only approaches.
  • Utilizing Radon transform for visual cue extraction.
  • Employing a floating cascade for outlier rejection and bootstrap aggregating (bagging) for combining local estimations.

Main Results:

  • Significant improvements in execution speed and estimation accuracy compared to state-of-the-art methods.
  • Demonstrated robustness to short and sparse text lines.
  • Effective handling of multiple different skews and the presence of non-textual objects.

Conclusions:

  • The proposed method offers a superior approach to document image skew angle estimation.
  • Its versatility makes it suitable for a wide range of real-world document analysis tasks.
  • The method provides a robust and efficient solution for challenging document images.