Captcha Recognition System based on Deep Learning

Abstract

In recent years, captcha recognition has emerged as a significant problem in the field of computer vision, aiming to enhance the machine's ability to recognize complex character images. It has widespread applications in areas such as network security and human-computer interaction. Traditional captcha recognition methods primarily rely on image processing and machine learning techniques. However, these methods exhibit certain limitations when dealing with complex captchas. Even a minor difference can lead to changes in their prediction results, thereby affecting the accuracy of captcha recognition and causing a significant drop in recognition accuracy. The advancement of deep learning technology has introduced new approaches and methods for captcha recognition. This paper proposes a deep learning-based captcha recognition system, which, by constructing complex validation sets and utilizing convolutional neural networks (convolutional neural network, CNN), trains on various types of captchas, effectively improving captcha recognition accuracy. Experimental results demonstrate that combining preprocessed datasets with CNN networks significantly enhances the stability of captcha recognition, achieving a recognition accuracy of over 95% and yielding promising experimental outcomes.