CAPTCHA RECOGNITION USING CNN

Abstract

Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is a widely used security mechanism designed to protect online services from automated bots and malicious activities. CAPTCHA systems present distorted text, characters, or images that are easy for humans to recognize but difficult for automated programs to interpret. However, advancements in deep learning have significantly improved the ability of machines to recognize and solve CAPTCHA challenges. This paper presents a CAPTCHA recognition framework using Convolutional Neural Networks (CNNs). The proposed system employs image preprocessing techniques such as noise removal, normalization, segmentation, and enhancement to improve CAPTCHA image quality before classification. A CNN architecture is utilized to automatically extract spatial features and recognize distorted characters with high accuracy. The model learns complex visual patterns from CAPTCHA datasets and effectively identifies characters despite variations in font styles, rotations, overlapping structures, and image distortions. Experimental analysis demonstrates that the CNN-based approach achieves superior recognition accuracy and computational efficiency compared to traditional image processing methods. The proposed framework highlights the effectiveness of deep learning techniques in automated CAPTCHA recognition and visual pattern analysis.