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基于CNN的验证码整体识别

Python 100.00%

captcha captcha-breaking cnn cnn-keras deep-learning

captcha's Introduction

DeepLab

机器学习、深度学习、量化投资，好用的工具、实用的工具，尽在DeepLab !

验证码识别

问题

通常验证码识别需要如下几个步骤：

（1）将整张图片切割为多个小图片，每个小图片包含一个字符

（2）训练模型，识别每个小图片中的字符

（3）将每个小图片的识别出的字符拼接为字符串作为整体识别结果

复杂一点的验证码往往各个字符不是均匀分布，难以准确切割。是否可以不切割图片直接识别其中的字符串？

思路

这其实是一个多标签分类问题，每个验证码图片有4个字符（标签），并且顺序固定；只要将卷积神经网络的最后一层softmax稍加修改就能实现多标签分类。

如下图所示，假设我们的验证码一共有4个字符,每个字符取26个大写字母中的一个；将卷积神经网络的输出层激活函数修改为sigmoid，输出层的[0-25]输出值对应第一个字符的onehot编码，[26-51]输出值对应第二个字符的onehot编码，[52-77]输出值对应第三个字符，[78-103]输出值对于第四个字符，并使用binary_crossentropy作为损失函数。

实验环境

keras theano cv2

训练集4000张图片，测试集2000张，每张图片大小20*80

网络结构

代码结构

generate.py 在GenPics目录下随机生成6000张验证码图片及其对应验证码字符串。

cnn_end2end_ocr.py 端到端验证码识别模型的训练、测试。

模型结果

Epoch = 60，Test Whole Accurate : 0.994

对于本文使用的较为简单的验证码，测试集整体正确率(4个字符都正确识别)99%以上。

参考：

https://zhuanlan.zhihu.com/p/21344595

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captcha's Issues

4-6个字符能用一个模型实现吗？

你好，你的模型我成功复制了，4个字符，6个字符单独测试都识别率很高，有没有办法一个模型既支持4个字符又支持6个字符。

numpy.core._internal.AxisError: axis 1 is out of bounds for array of dimension 1

按照你上面的代码运行发现报这个错误，查了下说明超出了范围，不大理解为什么连接相加的时候出错了，谢谢告知

fit error help

model = Sequential()

model.add(Conv2D(32, kernel_size=(5, 9),
activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 4)))

model.add(Conv2D(16, kernel_size=(5, 7), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 3)))

model.add(Flatten())

#num_classes*4 =104

model.add(Dense(num_classes*4, activation='sigmoid'))

model.compile(loss=keras.losses.binary_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])

model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))

ValueError: Error when checking target: expected dense_1 to have shape (104,) but got array with shape (26,)

why？ help me~~~~thks~

numpy.core._internal.AxisError: axis 1 is out of bounds for array of dimension 1

装好依赖，生成图片后，直接运行，报如下错误
picnum : 6000
6000 (20, 80)
6000 4
Traceback (most recent call last):
File "cnn_end2end_ocr.py", line 76, in
c = np.concatenate((c0,c1,c2,c3),axis=1)
numpy.core._internal.AxisError: axis 1 is out of bounds for array of dimension 1

验证码重叠

write labels err

writer.writerows(labels)

TypeError: a bytes-like object is required, not 'str'

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