输出结果
后期更新……
实现代码
后期更新……
image_ocr代码：DL之CNN：利用CNN(keras, CTC loss, {image_ocr})算法实现OCR光学字符识别
https://blog.csdn.net/qq_41185868/article/details/90239954
#DL之CNN：基于CNN-RNN(GRU,2)算法(keras+tensorflow)实现不定长文本识别
#Keras 的 CTC loss函数：位于 https://github.com/fchollet/keras/blob/master/keras/backend/tensorflow_backend.py文件中，内容如下：
import tensorflow as tf
from tensorflow.python.ops import ctc_ops as ctc
def ctc_batch_cost(y_true, y_pred, input_length, label_length):
"""Runs CTC loss algorithm on each batch element.
# Arguments
y_true: tensor `(samples, max_string_length)`
containing the truth labels.
y_pred: tensor `(samples, time_steps, num_categories)`
containing the prediction, or output of the softmax.
input_length: tensor `(samples, 1)` containing the sequence length for
each batch item in `y_pred`.
label_length: tensor `(samples, 1)` containing the sequence length for
each batch item in `y_true`.
# Returns
Tensor with shape (samples,1) containing the
CTC loss of each element.
"""
label_length = tf.to_int32(tf.squeeze(label_length))
input_length = tf.to_int32(tf.squeeze(input_length))
sparse_labels = tf.to_int32(ctc_label_dense_to_sparse(y_true, label_length))
y_pred = tf.log(tf.transpose(y_pred, perm=[1, 0, 2]) + 1e-8)
return tf.expand_dims(ctc.ctc_loss(inputs=y_pred, labels=sparse_labels, sequence_length=input_length), 1)
# 不定长文本识别
import os
import itertools
import re
import datetime
import cairocffi as cairo
import editdistance
import numpy as np
from scipy import ndimage
import pylab
from keras import backend as K
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers import Input, Dense, Activation, Reshape, Lambda
from keras.layers.merge import add, concatenate
from keras.layers.recurrent import GRU
from keras.models import Model
from keras.optimizers import SGD
from keras.utils.data_utils import get_file
from keras.preprocessing import image
from keras.callbacks import EarlyStopping,Callback
from keras.backend.tensorflow_backend import set_session
import tensorflow as tf
import matplotlib.pyplot as plt
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
set_session(tf.Session(config=config))
OUTPUT_DIR = 'image_ocr'
np.random.seed(55)
# # 从 Keras 官方文件中 import 相关的函数
# !wget https://raw.githubusercontent.com/fchollet/keras/master/examples/image_ocr.py
from image_ocr import *
#定义必要的参数：
run_name = datetime.datetime.now().strftime('%Y:%m:%d:%H:%M:%S')
start_epoch = 0
stop_epoch = 200
img_w = 128
img_h = 64
words_per_epoch = 16000
val_split = 0.2
val_words = int(words_per_epoch * (val_split))
# Network parameters
conv_filters = 16
kernel_size = (3, 3)
pool_size = 2
time_dense_size = 32
rnn_size = 512
input_shape = (img_w, img_h, 1)
# 使用这些函数以及对应参数构建生成器，生成不固定长度的验证码
fdir = os.path.dirname(get_file('wordlists.tgz', origin='http://www.mythic-ai.com/datasets/wordlists.tgz', untar=True))
img_gen = TextImageGenerator(monogram_file=os.path.join(fdir, 'wordlist_mono_clean.txt'),
bigram_file=os.path.join(fdir, 'wordlist_bi_clean.txt'),
minibatch_size=32, img_w=img_w, img_h=img_h,
downsample_factor=(pool_size ** 2), val_split=words_per_epoch - val_words )
#构建CNN网络
act = 'relu'
input_data = Input(name='the_input', shape=input_shape, dtype='float32')
inner = Conv2D(conv_filters, kernel_size, padding='same', activation=act, kernel_initializer='he_normal',
name='conv1')(input_data)
inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)
inner = Conv2D(conv_filters, kernel_size, padding='same', activation=act, kernel_initializer='he_normal',
name='conv2')(inner)
inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)
conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)
inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)
#减少输入尺寸到RNN：cuts down input size going into RNN:
inner = Dense(time_dense_size, activation=act, name='dense1')(inner)
#GRU模型：两层双向的算法
# Two layers of bidirecitonal GRUs
# GRU seems to work as well, if not better than LSTM:
gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)
gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)
gru1_merged = add([gru_1, gru_1b])
gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)
gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)
#将RNN输出转换为字符激活：transforms RNN output to character activations
inner = Dense(img_gen.get_output_size(), kernel_initializer='he_normal',
name='dense2')(concatenate([gru_2, gru_2b]))
y_pred = Activation('softmax', name='softmax')(inner)
Model(inputs=input_data, outputs=y_pred).summary()
labels = Input(name='the_labels', shape=[img_gen.absolute_max_string_len], dtype='float32')
input_length = Input(name='input_length', shape=[1], dtype='int64')
label_length = Input(name='label_length', shape=[1], dtype='int64')
#Keras目前不支持带有额外参数的loss funcs，所以CTC loss是在lambda层中实现的
# Keras doesn't currently support loss funcs with extra parameters, so CTC loss is implemented in a lambda layer
loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])
#clipnorm似乎加快了收敛速度：clipnorm seems to speeds up convergence
sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)
#计算损失发生在其他地方，所以使用一个哑函数来表示损失
# the loss calc occurs elsewhere, so use a dummy lambda func for the loss
model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)
if start_epoch > 0:
weight_file = os.path.join(OUTPUT_DIR, os.path.join(run_name, 'weights%02d.h5' % (start_epoch - 1)))
model.load_weights(weight_file)
#捕获softmax的输出，以便在可视化过程中解码输出
# captures output of softmax so we can decode the output during visualization
test_func = K.function([input_data], [y_pred])
# 反馈函数，即运行固定次数后，执行反馈函数可保存模型，并且可视化当前训练的效果
viz_cb = VizCallback(run_name, test_func, img_gen.next_val())
# 执行训练：
model.fit_generator(generator=img_gen.next_train(), steps_per_epoch=(words_per_epoch - val_words),
epochs=stop_epoch, validation_data=img_gen.next_val(), validation_steps=val_words,
callbacks=[EarlyStopping(patience=10), viz_cb, img_gen], initial_epoch=start_epoch)
