Your CAPTCHA Recognition Method Based on DEEP Learning Using MSER Descriptor

Abstract

Individuals and PCs (personal computers) can be recognized using CAPTCHAs (Completely Automated Public Turing test to distinguish Computers and Humans) which are mechanized for distinguishing them. Further, CAPTCHAs are intended to be solved by the people, but are unsolvable by the machines. As a result, using Convolutional Neural Networks (CNNs) these tests can similarly be unraveled. Moreover, the CNNs quality depends majorly on: the size of preparation set and the information that the classifier is found out on. Next, it is almost unmanageable to handle issue with CNNs. A new method of detecting CAPTCHA has been proposed, which simultaneously solves the challenges like preprocessing of images, proper segmentation of CAPTCHA using strokes, and the data training. The hyper parameters such as: Recall, Precision, Accuracy, Execution time, F-Measure (H-mean) and Error Rate are used for computation and comparison. In preprocessing, image enhancement and binarization are performed based on the stroke region of the CAPTCHA. The key points of these areas are based on the SURF feature. The exploratory outcomes show that the model has a decent acknowledgment impact on CAPTCHA with foundation commotion and character grip bending.

An Improved Text-Based and Image-Based CAPTCHA Based on Solving and燫esponse Time

“…A novel technique for CAPTCHA detection was proposed by [8], the technique simultaneously resolved preprocessing images and proper segmentation of CAPTCHA using stroke and data training. Accuracy, recall, precision, execution time, F-Measure and error rate were used as performance metrics.…”