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CAPTCHA Recognition Based on Faster R-CNN

机译:基于快速R-CNN的CAPTCHA识别

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In this paper, Faster R-CNN was employed to recognize the CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart). Unlike traditional method, the proposed method is based on deep learning object detection framework. By inputting the database into the network and training the Faster R-CNN, the feature map can be obtained through the convolutional layers. The proposed method can recognize the character and it is location. Experiments show that Faster R-CNN can be used in CAPTCHA recognition with promising speed and accuracy. The experimental results also show that the mAP (mean average precision) value will improve with the depth of the network increasing.
机译:在本文中,使用了Faster R-CNN来识别CAPTCHA(完全自动化的公共图灵测试,以区分计算机和人类)。与传统方法不同,该方法基于深度学习对象检测框架。通过将数据库输入网络并训练Faster R-CNN,可以通过卷积层获得特征图。所提出的方法可以识别字符并且是位置。实验表明,更快的R-CNN可以以有希望的速度和准确性用于CAPTCHA识别。实验结果还表明,随着网络深度的增加,mAP(平均平均精度)值将提高。

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