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Less is exponentially more

端到端的OCR:基于CNN的实现

OCR是一个古老的问题。这里我们考虑一类特殊的OCR问题,就是验证码的识别。传统做验证码的识别,需要经过如下步骤:

1. 二值化
2. 字符分割
3. 字符识别

这里最难的就是分割。如果字符之间有粘连,那分割起来就无比痛苦了。

最近研究深度学习,发现有人做端到端的OCR。于是准备尝试一下。一般来说目前做基于深度学习的OCR大概有如下套路:

1. 把OCR的问题当做一个多标签学习的问题。4个数字组成的验证码就相当于有4个标签的图片识别问题(这里的标签还是有序的),用CNN来解决。
2. 把OCR的问题当做一个语音识别的问题,语音识别是把连续的音频转化为文本,验证码识别就是把连续的图片转化为文本,用CNN+LSTM+CTC来解决。

目前第1种方法可以做到90%多的准确率(4个都猜对了才算对),第二种方法我目前的实验还只能到20%多,还在研究中。所以这篇文章先介绍第一种方法。

我们以python-captcha验证码的识别为例来做验证码识别。

下图是一些这个验证码的例子:

python-captcha

可以看到这里面有粘连,也有形变,噪音。所以我们可以看看用CNN识别这个验证码的效果。

首先,我们定义一个迭代器来输入数据,这里我们每次都直接调用python-captcha这个库来根据随机生成的label来生成相应的验证码图片。这样我们的训练集相当于是无穷大的。

class OCRIter(mx.io.DataIter):
def __init__(self, count, batch_size, num_label, height, width):
    super(OCRIter, self).__init__()
    self.captcha = ImageCaptcha(fonts=['./data/OpenSans-Regular.ttf'])
    self.batch_size = batch_size
    self.count = count
    self.height = height
    self.width = width
    self.provide_data = [('data', (batch_size, 3, height, width))]
    self.provide_label = [('softmax_label', (self.batch_size, num_label))]

def __iter__(self):
    for k in range(self.count / self.batch_size):
        data = []
        label = []
        for i in range(self.batch_size):
            # 生成一个四位数字的随机字符串
            num = gen_rand() 
            # 生成随机字符串对应的验证码图片
            img = self.captcha.generate(num)
            img = np.fromstring(img.getvalue(), dtype='uint8')
            img = cv2.imdecode(img, cv2.IMREAD_COLOR)
            img = cv2.resize(img, (self.width, self.height))
            cv2.imwrite("./tmp" + str(i % 10) + ".png", img)
            img = np.multiply(img, 1/255.0)
            img = img.transpose(2, 0, 1)
            data.append(img)
            label.append(get_label(num))

        data_all = [mx.nd.array(data)]
        label_all = [mx.nd.array(label)]
        data_names = ['data']
        label_names = ['softmax_label']

        data_batch = OCRBatch(data_names, data_all, label_names, label_all)
        yield data_batch

def reset(self):
    pass

然后我们用如下的网络来训练这个数据集:

def get_ocrnet():
    data = mx.symbol.Variable('data')
    label = mx.symbol.Variable('softmax_label')
    conv1 = mx.symbol.Convolution(data=data, kernel=(5,5), num_filter=32)
    pool1 = mx.symbol.Pooling(data=conv1, pool_type="max", kernel=(2,2), stride=(1, 1))
    relu1 = mx.symbol.Activation(data=pool1, act_type="relu")

    conv2 = mx.symbol.Convolution(data=relu1, kernel=(5,5), num_filter=32)
    pool2 = mx.symbol.Pooling(data=conv2, pool_type="avg", kernel=(2,2), stride=(1, 1))
    relu2 = mx.symbol.Activation(data=pool2, act_type="relu")

    conv3 = mx.symbol.Convolution(data=relu2, kernel=(3,3), num_filter=32)
    pool3 = mx.symbol.Pooling(data=conv3, pool_type="avg", kernel=(2,2), stride=(1, 1))
    relu3 = mx.symbol.Activation(data=pool3, act_type="relu")

    flatten = mx.symbol.Flatten(data = relu3)
    fc1 = mx.symbol.FullyConnected(data = flatten, num_hidden = 512)
    fc21 = mx.symbol.FullyConnected(data = fc1, num_hidden = 10)
    fc22 = mx.symbol.FullyConnected(data = fc1, num_hidden = 10)
    fc23 = mx.symbol.FullyConnected(data = fc1, num_hidden = 10)
    fc24 = mx.symbol.FullyConnected(data = fc1, num_hidden = 10)
    fc2 = mx.symbol.Concat(*[fc21, fc22, fc23, fc24], dim = 0)
    label = mx.symbol.transpose(data = label)
    label = mx.symbol.Reshape(data = label, target_shape = (0, ))
    return mx.symbol.SoftmaxOutput(data = fc2, label = label, name = "softmax")

上面这个网络要稍微解释一下。因为这个问题是一个有顺序的多label的图片分类问题。我们在fc1的层上面接了4个Full Connect层(fc21,fc22,fc23,fc24),用来对应不同位置的4个数字label。然后将它们Concat在一起。然后同时学习这4个label。目前用上面的网络训练,4位数字全部预测正确的精度可以达到90%左右。

全部的代码请参考 https://gist.github.com/xlvector/6923ef145e59de44ed06f21228f2f879

更新,经过比较长时间的训练,精度可以达到98%左右,最后几轮迭代的结果如下:

2016-05-22 21:58:34,859 Epoch[14] Batch [1250]  Speed: 117.29 samples/sec   Train-Accuracy=0.980800
2016-05-22 21:58:48,527 Epoch[14] Batch [1300]  Speed: 117.06 samples/sec   Train-Accuracy=0.982000
2016-05-22 21:59:02,174 Epoch[14] Batch [1350]  Speed: 117.24 samples/sec   Train-Accuracy=0.981200
2016-05-22 21:59:16,509 Epoch[14] Batch [1400]  Speed: 111.62 samples/sec   Train-Accuracy=0.976800
2016-05-22 21:59:31,031 Epoch[14] Batch [1450]  Speed: 110.18 samples/sec   Train-Accuracy=0.975600
2016-05-22 21:59:45,323 Epoch[14] Batch [1500]  Speed: 111.95 samples/sec   Train-Accuracy=0.975600
2016-05-22 21:59:59,634 Epoch[14] Batch [1550]  Speed: 111.81 samples/sec   Train-Accuracy=0.985600
2016-05-22 22:00:13,997 Epoch[14] Batch [1600]  Speed: 111.39 samples/sec   Train-Accuracy=0.978800
2016-05-22 22:00:28,270 Epoch[14] Batch [1650]  Speed: 112.11 samples/sec   Train-Accuracy=0.983200
2016-05-22 22:00:42,713 Epoch[14] Batch [1700]  Speed: 110.78 samples/sec   Train-Accuracy=0.985200
2016-05-22 22:00:56,668 Epoch[14] Batch [1750]  Speed: 114.65 samples/sec   Train-Accuracy=0.975600
2016-05-22 22:01:11,000 Epoch[14] Batch [1800]  Speed: 111.64 samples/sec   Train-Accuracy=0.981200
2016-05-22 22:01:25,450 Epoch[14] Batch [1850]  Speed: 110.73 samples/sec   Train-Accuracy=0.979600
2016-05-22 22:01:39,860 Epoch[14] Batch [1900]  Speed: 111.03 samples/sec   Train-Accuracy=0.978400
2016-05-22 22:01:54,272 Epoch[14] Batch [1950]  Speed: 111.02 samples/sec   Train-Accuracy=0.978800
2016-05-22 22:02:08,939 Epoch[14] Batch [2000]  Speed: 109.09 samples/sec   Train-Accuracy=0.981600
2016-05-22 22:02:08,939 Epoch[14] Resetting Data Iterator
2016-05-22 22:02:08,939 Epoch[14] Time cost=568.681
2016-05-22 22:02:14,124 Epoch[14] Validation-Accuracy=0.986000

另外这个Slide提供了关于深度学习进行验证码识别的详细描述。

更新 2016-05-31 :增加了inference的代码,所有代码在 https://github.com/xlvector/learning-dl/tree/master/mxnet/ocr