  CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans
Apart) can be used to protect data from auto bots. Countless kinds of CAPTCHAs
are thus designed, while we most frequently utilize text-based scheme because
of most convenience and user-friendly way \cite{bursztein2011text}. Currently,
various types of CAPTCHAs need corresponding segmentation to identify single
character due to the numerous different segmentation ways. Our goal is to
defeat the CAPTCHA, thus firstly the CAPTCHAs need to be split into character
by character. There isn't a regular segmentation algorithm to obtain the
divided characters in all kinds of examples, which means that we have to treat
the segmentation individually. In this paper, we build a whole system to defeat
the CAPTCHAs as well as achieve state-of-the-art performance. In detail, we
present our self-adaptive algorithm to segment different kinds of characters
optimally, and then utilize both the existing methods and our own constructed
convolutional neural network as an extra classifier. Results are provided
showing how our system work well towards defeating these CAPTCHAs.
