Open Access

ARTICLE

Radon CLF: A Novel Approach for Skew Detection Using Radon Transform

by Yuhang Chen¹, Mahdi Bahaghighat^2,*, Aghil Esmaeili Kelishomi³, Jingyi Du^1,*

1 Laboratory for Control Engineering, Xi’an University of Science &Technology, Xi’an, China
2 Computer Engineering Department, Imam Khomeini International University, Qazvin, Iran
3 MOE Key Laboratory for Intelligent and Network Security, Xi’an Jiaotong University, Xi’an, China

* Corresponding Authors: Mahdi Bahaghighat. Email: ; Jingyi Du. Email:

Computer Systems Science and Engineering 2023, 47(1), 675-697. https://doi.org/10.32604/csse.2023.038234

Received 03 December 2022; Accepted 03 March 2023; Issue published 26 May 2023

Abstract

In the digital world, a wide range of handwritten and printed documents should be converted to digital format using a variety of tools, including mobile phones and scanners. Unfortunately, this is not an optimal procedure, and the entire document image might be degraded. Imperfect conversion effects due to noise, motion blur, and skew distortion can lead to significant impact on the accuracy and effectiveness of document image segmentation and analysis in Optical Character Recognition (OCR) systems. In Document Image Analysis Systems (DIAS), skew estimation of images is a crucial step. In this paper, a novel, fast, and reliable skew detection algorithm based on the Radon Transform and Curve Length Fitness Function (CLF), so-called Radon CLF, was proposed. The Radon CLF model aims to take advantage of the properties of Radon spaces. The Radon CLF explores the dominating angle more effectively for a 1D signal than it does for a 2D input image due to an innovative fitness function formulation for a projected signal of the Radon space. Several significant performance indicators, including Mean Square Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Measure (SSIM), Accuracy, and run-time, were taken into consideration when assessing the performance of our model. In addition, a new dataset named DSI5000 was constructed to assess the accuracy of the CLF model. Both two- dimensional image signal and the Radon space have been used in our simulations to compare the noise effect. Obtained results show that the proposed method is more effective than other approaches already in use, with an accuracy of roughly 99.87% and a run-time of 0.048 (s). The introduced model is far more accurate and time-efficient than current approaches in detecting image skew.

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Keywords

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