NDLI: Capturing the Captcha: A Novel Technique to build Self updating Multi-domain Training dataset for Researchers

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Capturing the Captcha: A Novel Technique to build Self updating Multi-domain Training dataset for Researchers

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Capturing the Captcha: A Novel Technique to build Self updating Multi-domain Training dataset for Researchers

Content Provider	ACM Digital Library
Author	Srinivas, Sethuraman Dhar, Amit Kandasamy, Kamalanathan
Abstract	Captchas are widely used nowadays by many websites to check whether the user is human or a bot trying to be harmful. Lot of research has been done in the area of captchas. Whether the research is about strengthening the captcha, breaking captchas or optical character recognition, a large and accurate training dataset is needed for better results. Most of the research with captchas has been using either a limited training data set or self generated data set. In this paper, we present a novel method of building a self updating domain specific training dataset for Captchas, that can be used in various areas of research and development. Our technique is not limited to a particular kind of captcha. We prevent the misuse of this training data set using API keys that are issued only to trusted institutions/organizations. We further prevent the misuse by validating the domains during access to all our APIs.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450329088
DOI	10.1145/2660859.2660935
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-10-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Captcha Browser plugin Training data Machine learning Document object model
Content Type	Text
Resource Type	Article

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