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。

下载地址：文章末尾。

##环境依赖

1. 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
2. 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

1. 同时本文还依赖于numpy和opencv等常用库

 pip install numpy cv2

##知识准备

1. 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，这篇文章写得很
2. 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。

##正文 ###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)

###构建网络，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 layerw_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 layerw_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 = 0while 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