College of Computer Science and Information Technology, Central South University of Forestry and Technology, 498 shaoshan S Rd, Changsha, 410004, China.
Math Biosci Eng. 2019 Jun 24;16(5):5851-5861. doi: 10.3934/mbe.2019292.
Aiming at the problems of low efficiency and poor accuracy of traditional CAPTCHA recognition methods, we have proposed a more efficient way based on deep convolutional neural network (CNN). The Dense Convolutional Network (DenseNet) has shown excellent classification performance which adopts cross-layer connection. Not only it effectively alleviates the vanishing-gradient problem, but also dramatically reduce the number of parameters. However, it also has caused great memory consumption. So we improve and construct a new DenseNet for CAPTCHA recognition (DFCR). Firstly, we reduce the number of convolutional blocks and build corresponding classifiers for different types of CAPTCHA images. Secondly, we input the CAPTCHA images of TFrecords format into the DFCR for model training. Finally, we test the Chinese or English CAPTCHAs experimentally with different numbers of characters. Experiments show that the new network not only keeps the primary performance advantages of the DenseNets but also effectively reduces the memory consumption. Furthermore, the recognition accuracy of CAPTCHA with the background noise and character adhesion is above 99.9%.
针对传统 CAPTCHA 识别方法效率低、准确率差的问题，我们提出了一种基于深度卷积神经网络（CNN）的更有效的方法。密集卷积网络（DenseNet）采用跨层连接，表现出优异的分类性能。它不仅有效地缓解了梯度消失问题，而且大大减少了参数数量。然而，它也造成了巨大的内存消耗。因此，我们改进并构建了一个新的用于 CAPTCHA 识别的 DenseNet（DFCR）。首先，我们减少了卷积块的数量，并为不同类型的 CAPTCHA 图像构建了相应的分类器。其次，我们将 TFrecords 格式的 CAPTCHA 图像输入到 DFCR 中进行模型训练。最后，我们用不同数量的字符对中文或英文 CAPTCHA 进行了实验测试。实验表明，新网络不仅保持了 DenseNets 的主要性能优势，而且有效地降低了内存消耗。此外，具有背景噪声和字符粘连的 CAPTCHA 的识别准确率超过 99.9%。
