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首页> 外文会议>International Conference on Computer Vision, Image and Deep Learning >Chinese Character Captcha Sequential Selection System Based on Convolutional Neural Network
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Chinese Character Captcha Sequential Selection System Based on Convolutional Neural Network

机译：基于卷积神经网络的汉字CAPTCHA顺序选择系统

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摘要

To ensure security, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is widely used in people’s online lives. This paper presents a Chinese character captcha sequential selection system based on convolutional neural network (CNN). Captchas composed of English and digits can already be identified with extremely high accuracy, but Chinese character captcha recognition is still challenging. The task we need to complete is to identify Chinese characters with different colors and different fonts that are not on a straight line with rotation and affine transformation on pictures with complex backgrounds, and then perform word order restoration on the identified Chinese characters. We divide the task into several sub-processes: Chinese character detection based on Faster R-CNN, Chinese character recognition and word order recovery based on N-Gram. In the Chinese character recognition sub-process, we have made outstanding contributions. We constructed a single Chinese character data set and built a 10-layer convolutional neural network. Eventually we achieved an accuracy of 98.43%, and completed the task perfectly.
机译：为确保安全，完全自动化的公共图灵测试，告诉计算机和人类（CAPTCHA）被广泛用于人们的在线生活。本文介绍了基于卷积神经网络（CNN）的汉字CAPTCHA顺序选择系统。 CAPTCHAS由英语和数字组成的CAPTCHAS已经以极高的准确性识别，但汉字CAPTCHA识别仍然具有挑战性。我们需要完成的任务是识别具有不同颜色和不同字体的中文字符，这些字体不在直线上，在具有复杂背景的图片上的旋转和仿射转换，然后在所识别的汉字上执行Word订单恢复。我们将任务划分为几个子进程：基于更快的R-CNN，汉字识别和基于n克的词汇恢复的汉字检测。在汉字识别子进程中，我们取得了突出的贡献。我们构建了一个汉字数据集并建立了一个10层卷积神经网络。最终，我们实现了98.43％的准确性，完美地完成了任务。

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