最近在研究OCR識别相關的東西，最終目标是能識别身份證上的所有中文漢字+數字，不過本文先設定一個小目标，先識别定長為18的身份證号，當然本文的思路也是可以複用來識别定長的驗證碼識别的。 本文實作思路主要來源于Xlvector的部落格，采用基于CNN實作端到端的OCR，下面引用博文介紹目前基于深度學習的兩種OCR識别方法：
- 把OCR的問題當做一個多标簽學習的問題。4個數字組成的驗證碼就相當于有4個标簽的圖檔識别問題（這裡的标簽還是有序的），用CNN來解決。
- 把OCR的問題當做一個語音識别的問題，語音識别是把連續的音頻轉化為文本，驗證碼識别就是把連續的圖檔轉化為文本，用CNN+LSTM+CTC來解決。
這裡方法1主要用來解決固定長度标簽的圖檔識别問題，而方法2主要用來解決不定長度标簽的圖檔識别問題，本文實作方法1識别固定18個數字字元的身份證号。
環境依賴
- 本文基于tensorflow架構實作,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 進行訓練集圖檔的實時生成，同時後續也可擴充為能生成中文字元圖檔的訓練集，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)具體實作原理，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，這篇文章寫得很
- 本文實作思路很容易了解，就是把一個有序排列18個數字組成的圖檔當做一個多标簽學習的問題，标簽的長度可以任意改變，隻要是固定長度的，這個訓練方法都是适用的，當然現實中很多情況是需要識别不定長度的标簽的，這部分就需要使用方法2(CNN+lSTM+CTC)來解決了。
訓練資料集生成
首先先完成訓練資料集圖檔的生成，主要依賴于freetype-py庫生成數字/中文的圖檔。其中要注意的一點是就是生成圖檔的大小，本文經過多次嘗試後，生成的圖檔是32 x 256大小的，如果圖檔太大，則可能導緻訓練不收斂
生成出來的示例圖檔如下：
gen_image()方法傳回 image_data：圖檔像素資料 (32,256) label： 圖檔标簽 18位數字字元 477081933151463759 vec : 圖檔标簽轉成向量表示 (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 np
import freetype
import copy
import random
import cv2
class 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')
#随機生成字串，長度固定
#傳回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,傳回标簽和圖檔元素資料
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_)
#僅傳回單通道值，顔色對于漢字識别沒有什麼意義
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 text
if __name__ == '__main__':
genObj = gen_id_card()
image_data,label,vec = genObj.gen_image()
cv2.imshow('image', image_data)
cv2.waitKey(0)
建構網絡，開始訓練
首先定義生成一個batch的方法：
# 生成一個訓練batch
def 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，個人還不是很了解，讀者可自行百度，代碼來源于參考博文
#Batch Normalization? 有空再了解,tflearn or slim都有封裝
## http://stackoverflow.com/a/34634291/2267819
def 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和一層全連接配接層，卷積核分别是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CNN
def 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
最後執行訓練，使用sigmoid分類，每100次計算一次準确率，如果準确率超過80%，則儲存模型并結束訓練
# 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))
# 最後一層用來分類的softmax和sigmoid有什麼不同？
# optimizer 為了加快訓練 learning_rate應該開始大，然後慢慢衰
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，訓練準确率為：%s" % (step, acc)
# 如果準确率大80%,儲存模型,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次訓練後，得到準确率84.3%的結果
筆者在一開始訓練的時候圖檔大小是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，縮小圖檔為32 x 256後解決，不知道為啥，猜測要麼是網絡層級不夠，或者特征層數不夠吧。
小目标完成後，為了最終目标的完成，後續可能嘗試方法2，去識别不定長的中文字元圖檔，不過要先去了解LSTM網絡和 CTC模型了。
下載下傳位址：https://github.com/jimmyleaf/ocr_tensorflow_cnn/archive/master.zip