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 JCC  Vol.2 No.2 , January 2014
The Recognition of CAPTCHA
Abstract: CAPTCHA is a completely automated program designed to distinguish whether the user is a computer or human. As the problems of Internet security are worsening, it is of great significance to do research on CAPTCHA. This article starts from the recognition of CAPTCHAs, then analyses the weaknesses in its design and gives corresponding recognition proposals according to various weaknesses, finally offers suggestions related to the improvement of CAPTCHAs. Firstly, this article briefly introduces the basic steps during the decoding process and their principles. And during each step we choose methods which are better adapted to the features of different CAPTCHA images. Methods chosen are as followings: bimodal method in binarization, improved corrosion algorithm in denoising, projection segmentation method in denoised image processing and SVM in recognition. Then, we demonstrate detailed process through the samples taken from the online registration system of ICBC, show the recognition effect and correct the results according to the statistical data in the process. This article decodes CAPTCHAS from three other large banks in the same way but just provides the recognition results. Finally, this article offers targeted suggestions to the four banks based on the recognition effect and analysis process stated above.
Cite this paper: Wang, M. , Zhang, T. , Jiang, W. and Song, H. (2014) The Recognition of CAPTCHA. Journal of Computer and Communications, 2, 14-19. doi: 10.4236/jcc.2014.22003.
References

[1]   G. Yin, “The Recognition of CAPCHAs Based on SVM,” 2010

[2]   R.-E. Fan, P.-H. Chen and C.-J. Lin, “Working Set Selection Using Second Order Information for Training SVM,” Journal of Machine Leaning Research, Vol. 6, 2005, pp. 1889-1918.

[3]   N. Qu and L. Li, “Filtering Algorithm of Salt and Pepper Noise based on Fuzzy Theory,” Computer Knowledge and Technology, Vol. 5, No. 10, 2009, pp. 2699-2700.

[4]   X. Y. Wen, N. Gao, P. N. Xia and J. S. Jin, “Assorting Thoughts & Rcognition Technique of CAPCHAs,” Computer Engineering.

[5]   P. Lu, “Research and Application on SVM,” Hunan University, 2007.

[6]   L. Y. Wang, “Extraction and Classification of Image Features,” Xi’an Electronic and Engineering University, 2006.

[7]   L. Wang, R. Zhang, D. Yin, J. C. Zhan and C. Y. Wu, “Recognition of Touching Characters on CAPCHAs,” Journal of Computer Engineering and Applications, 2011.

[8]   R. L. Duan, Q. X. Li and Y. H. Li, “Summary for Methods for the Detection of Image’s Edge,” Optical Technology, 2005.

[9]   M. Yang, L. B. Zeng and D. C. Wang, “A Fast Algorithm for Mathematical Morphology and Corrosion & Expansion Operation,” Computer Engineering and Applications, 2005.

[10]   Q. L. Han, M. Zhu and Z. J. Yao, “Extraction of Image’s Characteristic Segment Based on Hough Transform,” Instrumentation Science, 2004.

[11]   H. Ji, J. X. Sun, X. F. Shao and L. Mao, “Outlook of Methods for Image Edge Extraction,” Computer Engineering and Applications, 2004.

 
 
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