id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-164703-lwwd8q3c	Noury, Zahra	Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability assessment	2020-06-15		.txt	text/plain	4595	250	57	One of the commonly used practices is using text-based CAPTCHAs. An example of these types of questions can be seen in Figure 2 , in which a sequence of random alphanumeric characters or digits or combinations of them are distorted and drawn in a noisy image. Geetika Garg and Chris Pollett [1] performed a trained Python-based deep neural network to crack fix-lengthed CAPTCHAs. The network consists of two Convolutional Maxpool layers, followed by a dense layer and a Softmax output layer. However, they have used three Convolutional layers followed by two dense layers and then the classifiers to solve six-digit CAPTCHAs. Besides, they have used a technique to reduce the size of the required training dataset. Also, we trained the network on 700,000 alphanumerical CAPTCHAs. For a better comparison and to have a more consistent approach, we only increased the number of neurons in each Softmax units from 10 to 31 to cover all common Latin characters and digits.	./cache/cord-164703-lwwd8q3c.txt	./txt/cord-164703-lwwd8q3c.txt