计算机工程与应用2016，Vol.52Issue(18)：1-7,7.DOI:10.3778/j.issn.1002-8331.1603-0181
卷积神经网络在验证码识别上的应用与研究
Research on captcha recognition with convolutional neural networks
摘要
Abstract
At present, captcha is widely used on the Internet. This paper introduces convolutional neural networks method to captcha recognition. By customizing new convolutional training networks, this paper uses k-means algorithm method for divisible idiom character segmentation, and through model pre-training to auto-generate images to enrich training dataset. Pre-training makes training network have a good initial value, thus speeds up the network convergence. Aiming at indivisible captcha, it leaves out character segmentation and inputs whole image to the convolutional networks model. It uses affine transformation, flood fill, and SVM classification or combines multi-label learning for captcha recognition. Finally, experi-mental study confirms that the method which is based on convolutional neural network, on average, achieves test accuracy of around 99%in the three types of CAPTCHA.关键词
卷积神经网络/验证码/模式识别/预训练/二值化Key words
convolutional neural networks/captcha/pattern recognition/pre-training/binarization分类
信息技术与安全科学引用本文复制引用
刘欢,邵蔚元,郭跃飞..卷积神经网络在验证码识别上的应用与研究[J].计算机工程与应用,2016,52(18):1-7,7.基金项目
上海市科委科技创新行动计划（No.14511106900）。 （）
