Abstract
Abstract: Tests that are automated and intended to tell humans from machines are called CAPTCHAs. Computers find it harder to solve them, which prevents programs from abusing online services and consuming internet resources, but humans can solve them with ease. Convolutional neural nets could be used to efficiently and automatically complete the CAPTCHA tests. The highly precise CAPTCHA recognition techniques currently in use can be structurally complex. Consequently, our group investigated an alternative method for resolving CAPTCHAs: enhancing accuracy by using image processing and a Convolutional Neural Network, which is more efficient in terms of run time and structural complexity. Our networks were also evaluated using CAPTCHA datasets that included background noise and character adhesion.
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