最近在研究OCR識別相關的東西，最終目標是能識別身份證上的所有中文漢字+數字，不過本文先設定一個小目標，先識別定長為18的身份證號，當然本文的思路也是可以復用來識別定長的驗證碼識別的。本文實現思路主要來源于Xlvector的博客，采用基于CNN實現端到端的OCR，下面引用博文介紹目前基于深度學習的兩種OCR識別方法：

• 把OCR的問題當做一個多標簽學習的問題。4個數字組成的驗證碼就相當于有4個標簽的圖片識別問題(這里的標簽還是有序的)，用CNN來解決。
• 把OCR的問題當做一個語音識別的問題，語音識別是把連續的音頻轉化為文本，驗證碼識別就是把連續的圖片轉化為文本，用CNN+LSTM+CTC來解決。

這里方法1主要用來解決固定長度標簽的圖片識別問題，而方法2主要用來解決不定長度標簽的圖片識別問題，本文實現方法1識別固定18個數字字符的身份證號。

? ?環境依賴? ? ??

本文基于tensorflow框架實現,依賴于tensorflow環境，建議使用anaconda進行python包管理及環境管理。本文使用freetype-py 進行訓練集圖片的實時生成，同時后續也可擴展為能生成中文字符圖片的訓練集，建議使用pip安裝。pip install freetype-py同時本文還依賴于numpy和opencv等常用庫 pip install numpy cv2

? ? 知識準備? ? ??

本文不具體介紹CNN (卷積神經網絡)具體實現原理，不熟悉的建議參看集智博文卷積：如何成為一個很厲害的神經網絡。

本文實現思路很容易理解，就是把一個有序排列18個數字組成的圖片當做一個多標簽學習的問題，標簽的長度可以任意改變，只要是固定長度的，這個訓練方法都是適用的，當然現實中很多情況是需要識別不定長度的標簽的，這部分就需要使用方法2(CNN+lSTM+CTC)來解決了。

訓練數據集生成?

首先先完成訓練數據集圖片的生成，主要依賴于freetype-py庫生成數字/中文的圖片。其中要注意的一點是就是生成圖片的大小，本文經過多次嘗試后，生成的圖片是32 x 256大小的，如果圖片太大，則可能導致訓練不收斂。生成出來的示例圖片如下：

gen_image()方法返回 image_data：圖片像素數據 (32,256) label：圖片標簽 18位數字字符 477081933151463759 vec : 圖片標簽轉成向量表示 (180,) 代表每個數字所處的列，總長度 18 * 10#!/usr/bin/env python2# -*- coding: utf-8 -*-"""身份證文字+數字生成類@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') #隨機生成字串，長度固定 #返回text,及對應的向量 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 #根據生成的text，生成image,返回標簽和圖片元素數據 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_) #僅返回單通道值，顏色對于漢字識別沒有什么意義 return image[:,:,2],text,vec #單字轉向量 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 #向量轉文本 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)

? ? 開始訓練? ? ??

首先定義生成一個batch的方法：

# 生成一個訓練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，個人還不是很理解，讀者可自行百度，代碼來源于參考博文#Batch Normalization? 有空再理解,tflearn or slim都有封裝## 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定義4層CNN和一層全連接層，卷積核分別是2層5x5、2層3x3，每層均使用tf.nn.relu非線性化，并使用max_pool，網絡結構讀者可自行調參優化# 定義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最后執行訓練，使用sigmoid分類，每100次計算一次準確率，如果準確率超過80%，則保存模型并結束訓練# 訓練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)) # 最后一層用來分類的softmax和sigmoid有什么不同？ # optimizer 為了加快訓練 learning_rate應該開始大，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計算一次準確率 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，訓練準確率為：%s" % (step, acc) # 如果準確率大80%,保存模型,完成訓練 if acc > 0.8: saver.save(sess, "crack_capcha.model", global_step=step) break step += 1執行結果，筆者在大概500次訓練后，得到準確率84.3%的結果

筆者在一開始訓練的時候圖片大小是64 x 512的，訓練的時候發現訓練速度很慢，而且訓練的loss不收斂一直保持在33左右，縮小圖片為32 x 256后解決，不知道為啥，猜測要么是網絡層級不夠，或者特征層數不夠吧。小目標完成后，為了最終目標的完成，后續可能嘗試方法2，去識別不定長的中文字符圖片，不過要先去理解LSTM網絡和 CTC模型了。下載地址：https://github.com/jimmyleaf/ocr_tensorflow_cnn/archive/master.zip

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