Document Image Skew Estimation
I. Installation
I.1. pip
pip install jdeskew
I.2. Docker
docker pull phamquiluan/jdeskew
II. How-to-use
II.1. using python
from jdeskew.estimator import get_angle
angle = get_angle(image)
from jdeskew.utility import rotate
output_image = rotate(image, angle)
cog
II.2. usingcog build --debug
cog predict -i input=@skew.png
# Output:
# Running prediction...
# {
# "angle": -0.12520868113522532
# }
Performance Comparison on DISE 2021
CE: Correct Estimation rate
WE: Worst Error
AED | TOP80 | CE | WE | |
---|---|---|---|---|
FredsDeskew | 10.82 | 0.09 | 0.54 | 109 |
PypiDeskew | 16.59 | 0.24 | 0.2 | 141 |
Koo, Hyung Il et al. | 0.22 | 0.09 | 0.48 | 9.43 |
CMC-MSU | 0.27 | 0.11 | 0.43 | 23.2 |
LRDE-EPITA-a | 0.14 | 0.06 | 0.66 | 10.61 |
Our (1024) | 0.11 | 0.07 | 0.67 | 1.13 |
Our (1500) | 0.09 | 0.05 | 0.78 | 1.13 |
Our (2048) | 0.08 | 0.04 | 0.84 | 1.13 |
Our (3072) | 0.07 | 0.04 | 0.86 | 1.13 |
Our (4096) | 0.08 | 0.04 | 0.83 | 1.18 |
Citation
L. Pham, T. A. Tran, "Document Image Skew Estimation using Adaptive Radial Projection", 2022.
@misc{luandise2022,
title={ADAPTIVE RADIAL PROJECTION ON FOURIER MAGNITUDE SPECTRUM FOR
DOCUMENT IMAGE SKEW ESTIMATION},
author={Luan Pham, Hao Hoang, Toan Mai, and Tuan Anh Tran},
url={https://github.com/phamquiluan/jdeskew},
year={2022}
}