CAPTCHA characters recognition based on image processing and support vector machine algorithms

Abstract

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is widely used in major mainstream websites as a security mechanism to classify human and computer. In recent years, the design and breaking technology of CAPTCHA have become an important research issue in order to verify security and reliability, which involves image processing, pattern recognition, artificial intelligence, computer vision and etc. This paper outlines some typical CAPTCHAs and presents an approach to recognize the text-based CAPTCHAs based on image processing and support vector machine algorithms. By testing the simple test-based CAPTCHAs, the experimental results show that the approach has an efficient recognition.

An Online Digital Imaging Excitation Sensor for Wind Turbine Gearbox Wear Condition Monitoring Based on Adaptive Deep Learning Method

“…This parameter can intuitively reflect the degree of contamination of the oil. The calculation formula is as follows, where R is the coverage area ratio, AF is the total number of pixels in the abrasive coverage area, and M and N are the length and width of the image, as in Equation (15).…”

“…W. Zhou et al [14] used Principal Component Analysis (PCA) to select wear particle feature parameters and improved the LS-SVM classifier based on a genetic algorithm, resulting in an increase in wear particle classification accuracy from 82.5% to 95%. L. Qiu et al [15] proposed a wear particle image recognition method based on a support vector machine, which applied the superiority of SVM in small sample classification to wear particle image recognition and achieved good results. W. Yuan, K. Chin, M. Hua et al [16,17] proposed an adaptive SVM recognition model based on an improved PSO algorithm and established the optimal adaptive SVM model by optimizing penalty parameters and kernel functions.…”