Telling humans and computers apart automatically
Communications of the ACM - Information cities
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization
Proceedings of the 14th ACM conference on Computer and communications security
Usability of CAPTCHAs or usability issues in CAPTCHA design
Proceedings of the 4th symposium on Usable privacy and security
A low-cost attack on a Microsoft captcha
Proceedings of the 15th ACM conference on Computer and communications security
Balancing usability and security in a video CAPTCHA
Proceedings of the 5th Symposium on Usable Privacy and Security
The robustness of a new CAPTCHA
Proceedings of the Third European Workshop on System Security
How Good Are Humans at Solving CAPTCHAs? A Large Scale Evaluation
SP '10 Proceedings of the 2010 IEEE Symposium on Security and Privacy
Attacks and design of image recognition CAPTCHAs
Proceedings of the 17th ACM conference on Computer and communications security
Proceedings of the 26th Annual Computer Security Applications Conference
Text-based CAPTCHA strengths and weaknesses
Proceedings of the 18th ACM conference on Computer and communications security
SEMAGE: a new image-based two-factor CAPTCHA
Proceedings of the 27th Annual Computer Security Applications Conference
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In this paper we present a novel approach for automatic segmentation and recognition of reCAPTCHA in Web sites. It is based on CAPTCHA image preprocessing with character alignment, morphological segmentation with three-color bar character encoding and heuristic recognition. The original proposal consists in exploiting three-color bar code for characters in CAPTCHA for their robust segmentation with presence of random collapse overlapping letters and distortions by particular patterns of waving rotation. Additionally, a novel implementation of SVM-based learning classifier for recognition of combinations of characters in training corpus has been proposed that permits to increment more than twice the recognition success rate without time extension of system response. The main goal of this research is to reduce vulnerability of CAPTCHA from spam and frauds as well as to provide a novel approach for recognizing either handwritten or degraded and damaged texts in ancient manuscripts. Our designed framework implementing the proposed approach has been tested in real-time applications with sites used CAPTCHAS achieving segmentation success rate about of 82% and recognition success rate about of 94%.