最近在研究OCR识别相关的东西，最终目标是能识别身份证上的所有中文汉字+数字，不过本文先设定一个小目标，先识别定长为18的身份证号，当然本文的思路也是可以复用来识别定长的验证码识别的。本文实现思路主要来源于Xlvector的博客，采用基于CNN实现端到端的OCR，下面引用博文介绍目前基于深度学习的两种OCR识别方法：

• 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。
• 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。

这里方法1主要用来解决固定长度标签的图片识别问题，而方法2主要用来解决不定长度标签的图片识别问题，本文实现方法1识别固定18个数字字符的身份证号。

   环境依赖

本文基于tensorflow框架实现,依赖于tensorflow环境，建议使用anaconda进行python包管理及环境管理。本文使用freetype-py 进行训练集图片的实时生成，同时后续也可扩展为能生成中文字符图片的训练集，建议使用pip安装。

pip install freetype-py

同时本文还依赖于numpy和opencv等常用库

 pip install numpy cv2

    知识准备

本文不具体介绍CNN (卷积神经网络)具体实现原理，不熟悉的建议参看集智博文卷积：如何成为一个很厉害的神经网络。

本文实现思路很容易理解，就是把一个有序排列18个数字组成的图片当做一个多标签学习的问题，标签的长度可以任意改变，只要是固定长度的，这个训练方法都是适用的，当然现实中很多情况是需要识别不定长度的标签的，这部分就需要使用方法2(CNN+lSTM+CTC)来解决了。

训练数据集生成

首先先完成训练数据集图片的生成，主要依赖于freetype-py库生成数字/中文的图片。其中要注意的一点是就是生成图片的大小，本文经过多次尝试后，生成的图片是32 x 256大小的，如果图片太大，则可能导致训练不收敛。生成出来的示例图片如下：

gen_image()方法返回 image_data：图片像素数据 (32,256) label：图片标签 18位数字字符 477081933151463759 vec : 图片标签转成向量表示 (180,) 代表每个数字所处的列，总长度 18 * 10

#!/usr/bin/env python2# -*- coding: utf-8 -*-"""身份证文字+数字生成类@author: pengyuanjie"""import numpy as npimport freetypeimport copyimport randomimport cv2class put_chinese_text(object): def __init__(self, ttf): self._face = freetype.Face(ttf) def draw_text(self, image, pos, text, text_size, text_color): ''' draw chinese(or not) text with ttf :param image: image(numpy.ndarray) to draw text :param pos: where to draw text :param text: the context, for chinese should be unicode type :param text_size: text size :param text_color:text color :return: image ''' self._face.set_char_size(text_size * 64) metrics = self._face.size ascender = metrics.ascender/64.0 #descender = metrics.descender/64.0 #height = metrics.height/64.0 #linegap = height - ascender + descender ypos = int(ascender) if not isinstance(text, unicode): text = text.decode('utf-8') img = self.draw_string(image, pos[0], pos[1]+ypos, text, text_color) return img def draw_string(self, img, x_pos, y_pos, text, color): ''' draw string :param x_pos: text x-postion on img :param y_pos: text y-postion on img :param text: text (unicode) :param color: text color :return: image ''' prev_char = 0 pen = freetype.Vector() pen.x = x_pos << 6 # div 64 pen.y = y_pos << 6 hscale = 1.0 matrix = freetype.Matrix(int(hscale)*0x10000L, int(0.2*0x10000L),\ int(0.0*0x10000L), int(1.1*0x10000L)) cur_pen = freetype.Vector() pen_translate = freetype.Vector() image = copy.deepcopy(img) for cur_char in text: self._face.set_transform(matrix, pen_translate) self._face.load_char(cur_char) kerning = self._face.get_kerning(prev_char, cur_char) pen.x += kerning.x slot = self._face.glyph bitmap = slot.bitmap cur_pen.x = pen.x cur_pen.y = pen.y - slot.bitmap_top * 64 self.draw_ft_bitmap(image, bitmap, cur_pen, color) pen.x += slot.advance.x prev_char = cur_char return image def draw_ft_bitmap(self, img, bitmap, pen, color): ''' draw each char :param bitmap: bitmap :param pen: pen :param color: pen color e.g.(0,0,255) - red :return: image ''' x_pos = pen.x >> 6 y_pos = pen.y >> 6 cols = bitmap.width rows = bitmap.rows glyph_pixels = bitmap.buffer for row in range(rows): for col in range(cols): if glyph_pixels[row*cols + col] != 0: img[y_pos + row][x_pos + col][0] = color[0] img[y_pos + row][x_pos + col][1] = color[1] img[y_pos + row][x_pos + col][2] = color[2]class gen_id_card(object): def __init__(self): #self.words = open('AllWords.txt', 'r').read().split(' ') self.number = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] self.char_set = self.number #self.char_set = self.words + self.number self.len = len(self.char_set) self.max_size = 18 self.ft = put_chinese_text('fonts/OCR-B.ttf') #随机生成字串，长度固定 #返回text,及对应的向量 def random_text(self): text = '' vecs = np.zeros((self.max_size * self.len)) #size = random.randint(1, self.max_size) size = self.max_size for i in range(size): c = random.choice(self.char_set) vec = self.char2vec(c) text = text + c vecs[i*self.len:(i+1)*self.len] = np.copy(vec) return text,vecs #根据生成的text，生成image,返回标签和图片元素数据 def gen_image(self): text,vec = self.random_text() img = np.zeros([32,256,3]) color_ = (255,255,255) # Write pos = (0, 0) text_size = 21 image = self.ft.draw_text(img, pos, text, text_size, color_) #仅返回单通道值，颜色对于汉字识别没有什么意义 return image[:,:,2],text,vec #单字转向量 def char2vec(self, c): vec = np.zeros((self.len)) for j in range(self.len): if self.char_set[j] == c: vec[j] = 1 return vec #向量转文本 def vec2text(self, vecs): text = '' v_len = len(vecs) for i in range(v_len): if(vecs[i] == 1): text = text + self.char_set[i % self.len] return textif __name__ == '__main__': genObj = gen_id_card() image_data,label,vec = genObj.gen_image() cv2.imshow('image', image_data) cv2.waitKey(0)

    开始训练

首先定义生成一个batch的方法：

# 生成一个训练batchdef get_next_batch(batch_size=128): obj = gen_id_card() batch_x = np.zeros([batch_size, IMAGE_HEIGHT*IMAGE_WIDTH]) batch_y = np.zeros([batch_size, MAX_CAPTCHA*CHAR_SET_LEN]) for i in range(batch_size):       image, text, vec = obj.gen_image()     batch_x[i,:] = image.reshape((IMAGE_HEIGHT*IMAGE_WIDTH))       batch_y[i,:] = vec return batch_x, batch_y

用了Batch Normalization，个人还不是很理解，读者可自行百度，代码来源于参考博文

#Batch Normalization? 有空再理解,tflearn or slim都有封装## http://stackoverflow.com/a/34634291/2267819def batch_norm(x, beta, gamma, phase_train, scope='bn', decay=0.9, eps=1e-5): with tf.variable_scope(scope):      #beta = tf.get_variable(name='beta', shape=[n_out], initializer=tf.constant_initializer(0.0), trainable=True)        #gamma = tf.get_variable(name='gamma', shape=[n_out], initializer=tf.random_normal_initializer(1.0, stddev), trainable=True)     batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments')     ema = tf.train.ExponentialMovingAverage(decay=decay)      def mean_var_with_update():         ema_apply_op = ema.apply([batch_mean, batch_var])          with tf.control_dependencies([ema_apply_op]):               return tf.identity(batch_mean), tf.identity(batch_var)      mean, var = tf.cond(phase_train, mean_var_with_update, lambda: (ema.average(batch_mean), ema.average(batch_var)))      normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, eps) return normed

定义4层CNN和一层全连接层，卷积核分别是2层5x5、2层3x3，每层均使用tf.nn.relu非线性化，并使用max_pool，网络结构读者可自行调参优化

# 定义CNNdef crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):   x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])   # 4 conv layer  w_c1 = tf.Variable(w_alpha*tf.random_normal([5, 5, 1, 32]))    b_c1 = tf.Variable(b_alpha*tf.random_normal([32])) conv1 = tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1)  conv1 = batch_norm(conv1, tf.constant(0.0, shape=[32]), tf.random_normal(shape=[32], mean=1.0, stddev=0.02), train_phase, scope='bn_1') conv1 = tf.nn.relu(conv1)  conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')   conv1 = tf.nn.dropout(conv1, keep_prob)    w_c2 = tf.Variable(w_alpha*tf.random_normal([5, 5, 32, 64]))   b_c2 = tf.Variable(b_alpha*tf.random_normal([64])) conv2 = tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2)  conv2 = batch_norm(conv2, tf.constant(0.0, shape=[64]), tf.random_normal(shape=[64], mean=1.0, stddev=0.02), train_phase, scope='bn_2') conv2 = tf.nn.relu(conv2)  conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')   conv2 = tf.nn.dropout(conv2, keep_prob)    w_c3 = tf.Variable(w_alpha*tf.random_normal([3, 3, 64, 64]))   b_c3 = tf.Variable(b_alpha*tf.random_normal([64])) conv3 = tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3)  conv3 = batch_norm(conv3, tf.constant(0.0, shape=[64]), tf.random_normal(shape=[64], mean=1.0, stddev=0.02), train_phase, scope='bn_3') conv3 = tf.nn.relu(conv3)  conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')   conv3 = tf.nn.dropout(conv3, keep_prob)    w_c4 = tf.Variable(w_alpha*tf.random_normal([3, 3, 64, 64]))   b_c4 = tf.Variable(b_alpha*tf.random_normal([64])) conv4 = tf.nn.bias_add(tf.nn.conv2d(conv3, w_c4, strides=[1, 1, 1, 1], padding='SAME'), b_c4)  conv4 = batch_norm(conv4, tf.constant(0.0, shape=[64]), tf.random_normal(shape=[64], mean=1.0, stddev=0.02), train_phase, scope='bn_4') conv4 = tf.nn.relu(conv4)  conv4 = tf.nn.max_pool(conv4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')   conv4 = tf.nn.dropout(conv4, keep_prob)    # Fully connected layer w_d = tf.Variable(w_alpha*tf.random_normal([2*16*64, 1024]))   b_d = tf.Variable(b_alpha*tf.random_normal([1024]))    dense = tf.reshape(conv4, [-1, w_d.get_shape().as_list()[0]])  dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d)) dense = tf.nn.dropout(dense, keep_prob)    w_out = tf.Variable(w_alpha*tf.random_normal([1024, MAX_CAPTCHA*CHAR_SET_LEN]))    b_out = tf.Variable(b_alpha*tf.random_normal([MAX_CAPTCHA*CHAR_SET_LEN]))  out = tf.add(tf.matmul(dense, w_out), b_out)   return out

最后执行训练，使用sigmoid分类，每100次计算一次准确率，如果准确率超过80%，则保存模型并结束训练

# 训练def train_crack_captcha_cnn():   output = crack_captcha_cnn()   # loss  #loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=Y)) loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y)) # 最后一层用来分类的softmax和sigmoid有什么不同？  # optimizer 为了加快训练 learning_rate应该开始大，然后慢慢衰  optimizer = tf.train.AdamOptimizer(learning_rate=0.002).minimize(loss)    predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])  max_idx_p = tf.argmax(predict, 2)  max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)   correct_pred = tf.equal(max_idx_p, max_idx_l)  accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))   saver = tf.train.Saver()   with tf.Session() as sess:      sess.run(tf.global_variables_initializer())     step = 0       while True:         batch_x, batch_y = get_next_batch(64)          _, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75, train_phase:True})         print(step, loss_)          # 每100 step计算一次准确率          if step % 100 == 0 and step != 0:                batch_x_test, batch_y_test = get_next_batch(100)               acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1., train_phase:False})              print "第%s步，训练准确率为：%s" % (step, acc)                # 如果准确率大80%,保存模型,完成训练               if acc > 0.8:                    saver.save(sess, "crack_capcha.model", global_step=step)                 break           step += 1

执行结果，笔者在大概500次训练后，得到准确率84.3%的结果

笔者在一开始训练的时候图片大小是64 x 512的，训练的时候发现训练速度很慢，而且训练的loss不收敛一直保持在33左右，缩小图片为32 x 256后解决，不知道为啥，猜测要么是网络层级不够，或者特征层数不够吧。小目标完成后，为了最终目标的完成，后续可能尝试方法2，去识别不定长的中文字符图片，不过要先去理解LSTM网络和 CTC模型了。下载地址：https://github.com/jimmyleaf/ocr_tensorflow_cnn/archive/master.zip

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