基于Faster-RCNN的极验点选式验证码识别

doi:10.16180/j.cnki.issn1007-7820.2019.09.009

Abstract

Abstract:

In view of the problem that traditional methods were difficult to recognize the geetest point-selective CAPTCHA , this paper proposed a recognition method that based on Faster-RCNN object detection model and convolutional neural network. Firstly, it improved the accuracy of text positioning by simplified RPN, then designed convolutional neural network to classify and identify texts,and trained the Tesseract-OCR identification databases to recognize the message texts. Finally, the result of texts recognition which embedded into background picture corresponded to the result of message texts recognition, then it reached the purpose of CAPTCHA recognition. Experimental results showed that the accuracy of this recognition method up to 72.4%.

Key words: geetest CAPTCHA recognition, Faster-RCNN, convolutional neural network, Tesseract-OCR

CLC Number:

TP391

ZHOU Wenkai,HAN Fang,KONG Weijian. Point-selective Geetest CAPTCHA Recognition Based on Faster-RCNN[J].Electronic Science and Technology, 2019, 32(9): 42-46.

Figures/Tables 10

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References 16

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特征提取网络	RPN	误识率	时间/ms
ZF网络	使用原始的RPN	25%	105
	使用改进的RPN	17%	94
VGG网络	使用原始的RPN	14.3%	198
	使用改进的RPN	9.4%	192

类型	层数	卷积核数量	卷积核大小	卷积核步长	输出大小
Conv1	2	64	3×3	1	64×64
Pool_1	1	64	3×3	2	32×32
Conv2	2	128	3×3	1	32×32
Pool_2	1	128	3×3	2	16×16
Conv3	3	256	3×3	1	16×16
Pool_3	1	256	3×3	2	8×8
Fc	1	-	-	-	4 096
Softmax	1	-	-	-	2 059

方法	识别率
不使用数据增广	92.25%
不使用批标准化	87.98%
唯一的预测值	95.66%
本文方法	97.94%

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Point-selective Geetest CAPTCHA Recognition Based on Faster-RCNN

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