Research Article

Transfer and Deep Learning-Based Gurmukhi Handwritten Word Classification Model

Table 2

Layer’s description of the proposed CNN model.

Model: “Sequential” layer (type)Output shapeParam#

conv2d (Conv2D)(None, 50, 50, 32)320
activation (Activation)(None, 50, 50, 32)0
batch normalization (Batch Normalization)(None, 50, 50, 32)128
max_pooling2d (MaxPooling2D)(None, 16, 16, 32)0
dropout (Dropout)(None, 16, 16, 32)0
conv2d_1 (Conv2D)(None, 16, 16, 64)18496
activation_1 (Activation)(None, 16, 16, 64)0
batch_normalization_1 (Batch Normalization)(None, 16, 16, 64)256
conv2d_2 (Conv2D)(None, 16, 16, 64)36928
activation_2 (Activation)(None, 16, 16, 64)0
batch_normalization_2 (Batch Normalization)(None, 16, 16, 64)256
max_pooling2d_1 (Max-Pooling 2D)(None, 8, 8, 64)0
dropout_1 (Dropout)(None, 8, 8, 64)0
conv2d_3 (Conv2D)(None, 8, 8, 128)73856
activation_3 (Activation)(None, 8, 8, 128)0
batch_normalization_3 (Batch Normalization)(None, 8, 8, 128)512
conv2d_4 (Conv2D)(None, 8, 8, 128)147584
activation_4 (Activation)(None, 8, 8, 128)0
batch_normalization_4 (Batch Normalization)(None, 8, 8, 128)512
max_pooling2d_2 (MaxPooling2D)(None, 4, 4, 128)0
dropout_2 (Dropout)(None, 4, 4, 128)0
flatten (Flatten)(None, 2048)0
dense (Dense)(None, 1024)2098176
activation_5 (Activation(None, 1024)0
batch_normalization_5 (Batch Normalization)(None, 1024)4096
dropout_3 (Dropout)(None, 1024)0
dense_1 (Dense)(None, 24)24600
activation_6 (Activation)(None, 24)0

Total params: 2,405,720, trainable params: 2,402,840, and nontrainable params: 2,880.