Your CAPTCHA Recognition Method Based on DEEP Learning Using MSER Descriptor

作     者：Deepak Kumar Ramandeep Singh Sukhvinder Singh Bamber 

作者机构：Faculty of Science and TechnologyLovely Professional UniversityPhagwara144411India Lovely Professional UniversityPhagwara144411India Department of Computer Science and EngineeringPanjab University SSG Regional CentreHoshiarpur146021India 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第8期

页      面：2981-2996页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Captcha MSER ANN SURF SNN 

摘      要：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 ***,CAPTCHAs are intended to be solved by the people,but are unsolvable by the *** a result,using Convolutional Neural Networks(CNNs)these tests can similarly be ***,the CNNs quality depends majorly on:the size of preparation set and the information that the classifier is found out ***,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 *** hyper parameters such as:Recall,Precision,Accuracy,Execution time,F-Measure(H-mean)and Error Rate are used for computation and *** preprocessing,image enhancement and binarization are performed based on the stroke region of the *** key points of these areas are based on the SURF *** exploratory outcomes show that the model has a decent acknowledgment impact on CAPTCHA with foundation commotion and character grip bending.