Develop A Ml Model Based Solution to Refine Captcha

Abstract

Developing a Machine Learning (ML)-based solution to refine CAPTCHA is an intelligent approach to improving the accuracy and efficiency of automated CAPTCHA recognition. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is widely used to protect online systems from bots and unauthorized access. However, traditional CAPTCHA recognition methods rely on manual feature extraction and rule-based techniques, which perform poorly on distorted, noisy, and complex CAPTCHA images. These limitations reduce recognition accuracy and increase processing time. The proposed system addresses these challenges by utilizing advanced deep learning techniques to automatically recognize and decode CAPTCHA images with high accuracy. The model is trained on a labeled CAPTCHA dataset using image preprocessing, feature extraction, and sequence prediction methods. Convolutional Neural Networks (CNN) are employed to extract visual features, while Convolutional Recurrent Neural Networks (CRNN) combined with Connectionist Temporal Classification (CTC) accurately recognize character sequences without requiring character-level segmentation. The system is developed using Python, TensorFlow, Keras, OpenCV, NumPy, and Flask for model implementation and deployment.