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An efficient segmentation algorithm for CAPTCHAs with line cluttering and character warping

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Abstract

A CAPTCHA is a test designed to distinguish computer programs from human beings, in order to prevent the abuse of networked resources. Academic research into CAPTCHAs includes designing friendly and secure CAPTCHA systems and defeating existing CAPTCHA systems. Traditionally, defeating a CAPTCHA test requires two procedures: segmentation and recognition. Recent research shows that the problem of segmentation is much harder than recognition. In this paper, two new segmentation techniques called projection and middle-axis point separation are proposed for CAPTCHAs with line cluttering and character warping. Experimental results show the proposed techniques can achieve segmentation rates of about 75%.

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References

  1. Baird HS, Bentley JL (2005) Implicit CAPTCHAs, in Proceedings of Document Recognition and Retrieval XII, pp. 191–196

  2. Blum M, von Ahn LA, Langford J (2000) The CAPTCHA project, “Completely automatic public turing test to tell computers and humans apart,” www.captcha.net, Dept. of Computer Science, Carnegie-Mellon Univ., and personal communications, November

  3. Chellapilla K, Larson K, Simard P, Czerwinski M (2005) Computers beat humans at single character recognition in reading based human interaction proofs (HIPs), in Proceedings of the Third Conference on E-Mail and Anti-Spam

  4. Chellapilla K, Simard P (2005) Using machine learning to break visual human interaction proofs (HIPs). In: Saul LK, Weiss Y, Bottou L (eds) Advances in neural information processing systems 17. MIT, Cambridge, pp 265–272

    Google Scholar 

  5. Coates AL, Baird HS, Fateman RJ (2001) Pessimal print: a reverse turing test, in Proceedings of the Sixth International Conference on Document Analysis and Recognition, pp. 1154–1158

  6. Hoque ME, Russomanno DJ, Yeasin M (2006) 2D Captchas from 3D models, in Proceedings of the IEEE SoutheastCon, pp. 165–170

  7. Huang SY, Lee YK, Bell G, Ou ZH (2008) A projection-based segmentation algorithm for breaking MSN and YAHOO CAPTCHAs, in Proceedings of the 2008 International Conference of Signal and Image Engineering (ICSIE’08), London, UK

  8. Misra D, Gaj K (2006) Face recognition CAPTCHAs, in Proceedings of the Advanced International Conference on Telecommunications and International Conference on Internet and Web Applications and Services, pp. 122–127

  9. Mori G, Malik J (2003) Recognizing objects in adversarial clutter: breaking a visual CAPTCHA, in Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 134–141

  10. Moy G, Jones N, Harkless C, Potter R (2004) Distortion estimation techniques in solving visual CAPTCHAs, in Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 23–28

  11. Shirali-Shahreza M, Shirali-Shahreza S (2006) Drawing CAPTCHA, in Proceedings of the 28th International Conference on Information Technology Interfaces, pp. 475–480

  12. Yan J, Ahmad ASE (2008) A low-cost attack on a Microsoft CAPTCHA, in Proceedings of 15th ACM Conference on Computer and Communications Security, Alexandria, Virginia, USA, ACM

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Ming Chuan University, 5 Teh-Ming Rd., Gwei Shan District, Taoyuan Country 333, Taipei, Taiwan

    Shih-Yu Huang, Yeuan-Kuen Lee, Graeme Bell & Zhan-he Ou

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  1. Shih-Yu Huang
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  2. Yeuan-Kuen Lee
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  3. Graeme Bell
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  4. Zhan-he Ou
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Correspondence to Shih-Yu Huang.

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Huang, SY., Lee, YK., Bell, G. et al. An efficient segmentation algorithm for CAPTCHAs with line cluttering and character warping. Multimed Tools Appl 48, 267–289 (2010). https://doi.org/10.1007/s11042-009-0341-5

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  • DOI: https://doi.org/10.1007/s11042-009-0341-5

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