Text based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is the most widely used mechanism adopted by numerous popular web sites in order to differentiate between machines and humans, however due to extensive research carried out by computer vision researchers, it is now a days vulnerable against automated attacks. Segmentation is the most difficult task in automatic recognition of CAPTCHAs, therefore contemporary Text based CAPTCHAs try to combine the characters together in order to make them as segmentation resistant against these attacks as possible. In this research, we have found vulnerabilities in such CAPTCHAs, a novel mechanism, i.e. the recognition based segmentation is applied to crop such connected characters, a sliding window based neural network classifier is used to recognize and segment the connected characters. Experimental results have proved 95.5% recognition success rate and 58.25% segmentation success rate on our dataset of tmall CAPTCHAs, this algorithm is further tested on two other datasets of slightly different implementations and promising results were achieved.