微型电脑应用

为了降低干扰信息对验证码识别效果的影响，提高不定长验证码的识别准确率，设计基于PyTorch框架的不定长验证码抗干扰识别系统。系统由采集模块、处理模块、识别模块三部分组成。采集模块利用网络爬虫获取验证码图像，并在图像处理模块中完成灰度化处理。调用全局阈值法对处理后的图像实施二值化操作，区分图像背景与字符。将采用滑动窗口法在去除噪声后的验证码图像中输入基于PyTorch框架的验证码识别模块，利用改进的ResNet-18网络提取图像特征后，通过长短期记忆网络模型获取字符序列特征，利用时序分类算法完成标签的对齐，实现对不定长验证码的抗干扰识别。实验结果表明，所设计系统可以有效实现对验证码图像的灰度化及去噪处理，并完成含不同程度干扰信息的不定长验证码的准确识别。

In order to reduce the influence of interference information on verification code recognition and improve the identification accuracy of indefinite length verification code, an anti-interference identification system of indefinite length verification code based on PyTorch framework is designed. The system consists of acquisition module, processing module and recognition module. The acquisition module uses the Web crawler to obtain the verification code image, and completes the gray processing in the image processing module. The global threshold method is used to implement binarization operation on the processed image to distinguish the image background and characters. In the verification code image after noise removal by sliding window method is input into the verification code recognition module based on PyTorch framework. After extracting the image features by the improved ResNet-18, the character sequence features are obtained through the long short-term memory network model, and the label alignment is completed by the sequential classification algorithm. The anti-interference identification of indefinite length verification code is realized. Experimental results show that the designed system can effectively grayscale and denoise verification code images, and complete the accurate recognition of the indefinite length verification code with different degrees of interference information.