def bluche(input_size, d_model, learning_rate):
"""
Gated Convolucional Recurrent Neural Network by Bluche et al.
Reference:
Bluche, T., Messina, R.:
Gated convolutional recurrent neural networks for multilingual handwriting recognition.
In: Document Analysis and Recognition (ICDAR), 2017
14th IAPR International Conference on, vol. 1, pp. 646–651, 2017.
URL: https://ieeexplore.ieee.org/document/8270042
"""
input_data = Input(name="input", shape=input_size)
cnn = Reshape((input_size[0] // 2, input_size[1] // 2, input_size[2] * 4))(input_data)
cnn = Conv2D(filters=8, kernel_size=(3,3), strides=(1,1), padding="same", activation="tanh")(cnn)
cnn = Conv2D(filters=16, kernel_size=(2,4), strides=(2,4), padding="same", activation="tanh")(cnn)
cnn = GatedConv2D(filters=16, kernel_size=(3,3), strides=(1,1), padding="same")(cnn)
cnn = Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding="same", activation="tanh")(cnn)
cnn = GatedConv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding="same")(cnn)
cnn = Conv2D(filters=64, kernel_size=(2,4), strides=(2,4), padding="same", activation="tanh")(cnn)
cnn = GatedConv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding="same")(cnn)
cnn = Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding="same", activation="tanh")(cnn)
cnn = MaxPooling2D(pool_size=(1,4), strides=(1,4), padding="valid")(cnn)
shape = cnn.get_shape()
blstm = Reshape((shape[1], shape[2] * shape[3]))(cnn)
blstm = Bidirectional(LSTM(units=128, return_sequences=True))(blstm)
blstm = Dense(units=128, activation="tanh")(blstm)
blstm = Bidirectional(LSTM(units=128, return_sequences=True))(blstm)
output_data = Dense(units=d_model, activation="softmax")(blstm)
if learning_rate is None:
learning_rate = 4e-4
optimizer = RMSprop(learning_rate=learning_rate)
return (input_data, output_data, optimizer)
def bluche(input_size, output_size, learning_rate=4e-4):
"""
Gated Convolucional Recurrent Neural Network by Bluche et al.
Reference:
Bluche, T., Messina, R.:
Gated convolutional recurrent neural networks for multilingual handwriting recognition.
In: Document Analysis and Recognition (ICDAR), 2017
14th IAPR International Conference on, vol. 1, pp. 646–651, 2017.
URL: https://ieeexplore.ieee.org/document/8270042
Moysset, B. and Messina, R.:
Are 2D-LSTM really dead for offline text recognition?
In: International Journal on Document Analysis and Recognition (IJDAR)
Springer Science and Business Media LLC
URL: http://dx.doi.org/10.1007/s10032-019-00325-0
"""
input_data = Input(name="input", shape=input_size)
cnn = Reshape((input_size[0] // 2, input_size[1] // 2,
input_size[2] * 4))(input_data)
cnn = Conv2D(filters=8, kernel_size=(3, 3), strides=(1, 1),
padding="same")(cnn)
cnn = Activation(activation="tanh")(cnn)
cnn = Conv2D(filters=16,
kernel_size=(2, 4),
strides=(2, 4),
padding="same")(cnn)
cnn = Activation(activation="tanh")(cnn)
cnn = GatedConv2D(filters=16,
kernel_size=(3, 3),
strides=(1, 1),
padding="same")(cnn)
cnn = Conv2D(filters=32,
kernel_size=(3, 3),
strides=(1, 1),
padding="same")(cnn)
cnn = Activation(activation="tanh")(cnn)
cnn = GatedConv2D(filters=32,
kernel_size=(3, 3),
strides=(1, 1),
padding="same")(cnn)
cnn = Conv2D(filters=64,
kernel_size=(2, 4),
strides=(2, 4),
padding="same")(cnn)
cnn = Activation(activation="tanh")(cnn)
cnn = GatedConv2D(filters=64,
kernel_size=(3, 3),
strides=(1, 1),
padding="same")(cnn)
cnn = Conv2D(filters=128,
kernel_size=(3, 3),
strides=(1, 1),
padding="same")(cnn)
cnn = Activation(activation="tanh")(cnn)
cnn = MaxPooling2D(pool_size=(1, 4), strides=(1, 4), padding="valid")(cnn)
shape = cnn.get_shape()
blstm = Reshape((shape[1], shape[2] * shape[3]))(cnn)
blstm = Bidirectional(LSTM(units=128, return_sequences=True,
dropout=0.5))(blstm)
blstm = Dense(units=128)(blstm)
blstm = Activation(activation="tanh")(blstm)
blstm = Bidirectional(LSTM(units=128, return_sequences=True,
dropout=0.5))(blstm)
blstm = Dense(units=output_size)(blstm)
output_data = Activation(activation="softmax")(blstm)
optimizer = RMSprop(learning_rate=learning_rate)
return (input_data, output_data, optimizer)