Analysis of Text-CAPTCHA Using Machine Learning

Text-based CAPTCHAs are computer-administered challenge-response examinations designed to distinguish between human beings and automated systems. Users must decipher and input distorted textual characters into designated fields to successfully fulfill text-based CAPTCHA protocols. Gimpy, EZ-Gimpy, Baffle Text, Scatter type, Clickable CAPTCHA, and strangeness in word all fall under subcategories which may be utilized for organizing text-based CAPTCHAs. Each one of these classes has specific challenges, such as bypasses. Altering the text’s rotation, distribution, and distortion may be utilized to address the challenges related to CAPTCHAs. Clickable CAPTCHAs introduce a new era of CAPTCHAs, without a doubt. This study delves further into the authors’ conclusions and language, while also providing assessments of previously published publications that include a range of CAPTCHA dataset types. Deep learning and machine learning are crucial for enhancing user security, including notable models such as CNN, R-CNN, ANN, and others. Automated bots have made a significant impact on user safety in recent years. The objective of this research is to present a comprehensive analysis of the prevailing pattern of CAPTCHAs, together with their advantages and disadvantages.