.png){kind=icon}
返回
Selective Learning Confusion Class for Text-Based CAPTCHA Recognition
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2899044.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) recognition is one of the most important branches in CAPTCHA research. The existing CAPTCHA recognition methods based on Deep Convolutional Neural Network (DCNN) have low recognition accuracy in confusion class. To solve this problem, a novel method of selective learning confusion class for text-based CAPTCHA recognition is presented. First, a frame with two-stage DCNN is proposed, which integrates all-class DCNN and confusion-class DCNN. Second, a confusion relation matrix is constructed to show confusion relations among classes, which can be used to analyze the output of all-class DCNN. Third, a set partition algorithm is presented, which can be used to divide a confusion class set into multiple subsets, each one corresponding to a new confusion-class DCNN. Fourth, with a view on improving the recognition accuracy of the confusing characters in confusion-class DCNN, training and validating interactive learning algorithm is proposed. Lastly, the outputs of the two stages were combined as the final recognition result. The experimental results based on real CAPTCHA data sets demonstrate that, compared with the four state-of-the-art attacks, the proposed method could effectively improve text-based CAPTCHA recognition accuracy by 1.4%-39.4%.
Keyword:
Character classification
confusion class
deep learning
selective learning
text-based CAPTCHA recognition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Investigation of Ti mesh-supported anodes for direct borohydride fuel cells直接硼氢化物燃料电池用钛网支撑阳极的研究
PREAI
分享收藏DropSample: A new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten Chinese character recognition.png)
PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏
查看更多内容