Datasets:
HenryAI
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KerasCodeExamples.txt

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model = build_model()
model.summary()
Model: \"handwriting_recognizer\"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
image (InputLayer) [(None, 128, 32, 1)] 0
__________________________________________________________________________________________________
Conv1 (Conv2D) (None, 128, 32, 32) 320 image[0][0]
__________________________________________________________________________________________________
pool1 (MaxPooling2D) (None, 64, 16, 32) 0 Conv1[0][0]
__________________________________________________________________________________________________
Conv2 (Conv2D) (None, 64, 16, 64) 18496 pool1[0][0]
__________________________________________________________________________________________________
pool2 (MaxPooling2D) (None, 32, 8, 64) 0 Conv2[0][0]
__________________________________________________________________________________________________
reshape (Reshape) (None, 32, 512) 0 pool2[0][0]
__________________________________________________________________________________________________
dense1 (Dense) (None, 32, 64) 32832 reshape[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 32, 64) 0 dense1[0][0]
__________________________________________________________________________________________________
bidirectional (Bidirectional) (None, 32, 256) 197632 dropout[0][0]
__________________________________________________________________________________________________
bidirectional_1 (Bidirectional) (None, 32, 128) 164352 bidirectional[0][0]
__________________________________________________________________________________________________
label (InputLayer) [(None, None)] 0
__________________________________________________________________________________________________
dense2 (Dense) (None, 32, 81) 10449 bidirectional_1[0][0]
__________________________________________________________________________________________________
ctc_loss (CTCLayer) (None, 32, 81) 0 label[0][0]
dense2[0][0]
==================================================================================================
Total params: 424,081
Trainable params: 424,081
Non-trainable params: 0
__________________________________________________________________________________________________
Evaluation metric
Edit Distance is the most widely used metric for evaluating OCR models. In this section, we will implement it and use it as a callback to monitor our model.
We first segregate the validation images and their labels for convenience.
validation_images = []
validation_labels = []
for batch in validation_ds:
validation_images.append(batch[\"image\"])
validation_labels.append(batch[\"label\"])
Now, we create a callback to monitor the edit distances.
def calculate_edit_distance(labels, predictions):
# Get a single batch and convert its labels to sparse tensors.
saprse_labels = tf.cast(tf.sparse.from_dense(labels), dtype=tf.int64)
# Make predictions and convert them to sparse tensors.
input_len = np.ones(predictions.shape[0]) * predictions.shape[1]
predictions_decoded = keras.backend.ctc_decode(
predictions, input_length=input_len, greedy=True
)[0][0][:, :max_len]
sparse_predictions = tf.cast(
tf.sparse.from_dense(predictions_decoded), dtype=tf.int64
)
# Compute individual edit distances and average them out.
edit_distances = tf.edit_distance(
sparse_predictions, saprse_labels, normalize=False
)
return tf.reduce_mean(edit_distances)
class EditDistanceCallback(keras.callbacks.Callback):
def __init__(self, pred_model):
super().__init__()
self.prediction_model = pred_model
def on_epoch_end(self, epoch, logs=None):
edit_distances = []
for i in range(len(validation_images)):
labels = validation_labels[i]
predictions = self.prediction_model.predict(validation_images[i])
edit_distances.append(calculate_edit_distance(labels, predictions).numpy())
print(
f\"Mean edit distance for epoch {epoch + 1}: {np.mean(edit_distances):.4f}\"
)
Training
Now we are ready to kick off model training.
epochs = 10 # To get good results this should be at least 50.
model = build_model()
prediction_model = keras.models.Model(
model.get_layer(name=\"image\").input, model.get_layer(name=\"dense2\").output
)
edit_distance_callback = EditDistanceCallback(prediction_model)
# Train the model.
history = model.fit(
train_ds,