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Adaptive Median Filtering Algorithm Based on Divide and Conquer and Its Application in CAPTCHA Recognition

Wentao Ma1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Yuanjing Luo1, Neal N. Xiong2

College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha, 410114, China.
Department of Mathematics and Computer Science, Northeastern State University, Oklahoma, 74464, USA.

* Corresponding Author: Jiaohua Qin. Email: .

Computers, Materials & Continua 2019, 58(3), 665-677.


As the first barrier to protect cyberspace, the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks. By researching the CAPTCHA, we can find its vulnerability and improve the security of CAPTCHA. Recently, many studies have shown that improving the image preprocessing effect of the CAPTCHA, which can achieve a better recognition rate by the state-of-the-art machine learning algorithms. There are many kinds of noise and distortion in the CAPTCHA images of this experiment. We propose an adaptive median filtering algorithm based on divide and conquer in this paper. Firstly, the filtering window data quickly sorted by the data correlation, which can greatly improve the filtering efficiency. Secondly, the size of the filtering window is adaptively adjusted according to the noise density. As demonstrated in the experimental results, the proposed scheme can achieve superior performance compared with the conventional median filter. The algorithm can not only effectively detect the noise and remove it, but also has a good effect in preservation details. Therefore, this algorithm can be one of the most strong tools for various CAPTCHA image recognition and related applications.


Cite This Article

W. Ma, J. Qin, X. Xiang, Y. Tan, Y. Luo et al., "Adaptive median filtering algorithm based on divide and conquer and its application in captcha recognition," Computers, Materials & Continua, vol. 58, no.3, pp. 665–677, 2019.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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