Efficient CAPTCHA Image Recognition Using Convolutional Neural Networks and Long Short-Term Memory Networks

Abstract

This research paper presents a novel approach to CAPTCHA image recognition using a hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs). CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems are widely employed to differentiate between human users and bots in online applications, ensuring security and preventing automated attacks. The purpose of this study is to develop an efficient and accurate CAPTCHA recognition system capable of handling complex and distorted CAPTCHA images commonly used on websites. The methodology involves preprocessing CAPTCHA images, including cropping and resizing, followed by feature extraction using CNNs to capture spatial patterns and structures. The extracted features are then fed into LSTMs to model temporal dependencies and sequence information, enabling the model to recognize characters in the CAPTCHA image. The proposed model is trained and evaluated using a dataset consisting of various CAPTCHA images sourced from online platforms. The main results demonstrate the effectiveness of the CNN-LSTM hybrid approach in accurately recognizing characters within CAPTCHA images. The model achieves high accuracy rates in deciphering distorted and noisy CAPTCHA images, outperforming baseline models and existing state-of-the-art methods. Additionally, the model exhibits robustness to variations in CAPTCHA designs and backgrounds, making it suitable for real-world applications requiring robust CAPTCHA recognition.