Text-based CAPTCHAs (completely automated public Turing test to tell computers and humans apart) are widely used to prevent unauthorized access by bots. However, there have been advancements in image segmentation and character recognition techniques, which can be used for bot access; therefore, distorted characters that are difficult even for humans to recognize are often utilized. Thus, a new text-based CAPTCHA technology with anti-segmentation properties is required. In this study, we propose CAPTCHA that uses stereoscopy based on binocular disparity. Generating a character area and its background with the identical color patterns, it becomes impossible to extract the character regions if the left and right images are analyzed separately, which is a huge advantage of our method. However, character regions can be extracted by using disparity estimation or subtraction processing using both images; thus, to prevent such attacks, we intentionally add noise to the image. The parameters characterizing the amount of added noise are adjusted based on experiments with subjects wearing a head-mounted display to realize stereo vision. With optimal parameters, the recognition rate reaches 0.84; moreover, sufficient robustness against bot attacks is achieved.