Comments (6)
是的,不需要,这是一个端到端的深度学习方法
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所以这个应该不能去直接识别网站上面的验证码吧,只是一种学习的理想模型
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我看了一下网站验证码方面的论文 发现难点都在图片的干扰线 噪声 的去除 以及粘连字符的分割
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能,只要你准备十万样本就好,不需要任何特征工程,你说的处理技术是上个时代的东西了。
你可以认真阅读文章,遇到不会的词就搜一下概念
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谢谢您的回答 , 所以你的意思应该是把上面多个字符当成一个整体作为label吗? 比如验证码图片上面是5733, 训练的label就是5733
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是的,你可以去查一下端到端(end to end)的定义,我相信你能够理解它的概念
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Related Issues (20)
- 如何把保存下来的ctc模型载入继续训练呢?
- 您可否告知下这几个文件是独立运行的吗,如何训练自己的中文验证码呢 HOT 5
- RNN分类之后, 在评估处怎么获得每个字符的概率呢 HOT 1
- 楼主您好,请问3500常用汉字的验证码识别,该模型大小够吗? HOT 5
- 多行验证码如何识别呢? HOT 4
- loss为负数且不断减小
- cannot import name '_imaging' from 'PIL'
- 变长标签怎么处理
- 如果验证码最后两位相同,似乎一定识别错误 HOT 4
- 运行winpy/main.py遇到的问题 HOT 5
- train和val的acc都可以到99%,但是eval,只有0.00265,这是怎么回事呢
- 尝试把n_class+1程序可以运行,但是不知道对不对 HOT 1
- 请问如果是不定长的验证码 可以使用吗 HOT 2
- 使用CTC, 识别时不限制4个字符长度,识别率如何? HOT 3
- CTC模型不定长输出问题 HOT 1
- 效果不理想
- 训练完了怎么用啊,纯小白 HOT 2
- cnn_2019.ipynb(防止 tensorflow 占用所有显存)tensorflow2.0要怎么改 HOT 1
- tensorflow 2.0 训练的时候 日志不显示 不知道 训练到哪一步 HOT 1
- 请问我改如何替换掉ctc_2019中的lambda方法呢 因为lambda在加载保存的模型会有错误 HOT 1
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