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Re-updating OCR model for reading Captchas Keras 3 example (TF-Only) #1843

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Re-updating OCR model for reading Captchas Keras 3 example (TF-Only) #1843

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00bb6a3
keras ctc_decode ops added
sitamgithub-MSIT Apr 25, 2024
0518168
small fix to var name
sitamgithub-MSIT Apr 25, 2024
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34 changes: 6 additions & 28 deletions examples/vision/captcha_ocr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -359,30 +359,6 @@ def build_model():
"""


def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1):
input_shape = ops.shape(y_pred)
num_samples, num_steps = input_shape[0], input_shape[1]
y_pred = ops.log(ops.transpose(y_pred, axes=[1, 0, 2]) + keras.backend.epsilon())
input_length = ops.cast(input_length, dtype="int32")

if greedy:
(decoded, log_prob) = tf.nn.ctc_greedy_decoder(
inputs=y_pred, sequence_length=input_length
)
else:
(decoded, log_prob) = tf.compat.v1.nn.ctc_beam_search_decoder(
inputs=y_pred,
sequence_length=input_length,
beam_width=beam_width,
top_paths=top_paths,
)
decoded_dense = []
for st in decoded:
st = tf.SparseTensor(st.indices, st.values, (num_samples, num_steps))
decoded_dense.append(tf.sparse.to_dense(sp_input=st, default_value=-1))
return (decoded_dense, log_prob)


# Get the prediction model by extracting layers till the output layer
prediction_model = keras.models.Model(
model.input[0], model.get_layer(name="dense2").output
Expand All @@ -394,12 +370,14 @@ def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1):
def decode_batch_predictions(pred):
input_len = np.ones(pred.shape[0]) * pred.shape[1]
# Use greedy search. For complex tasks, you can use beam search
results = ctc_decode(pred, input_length=input_len, greedy=True)[0][0][
:, :max_length
]
results = ops.ctc_decode(pred, sequence_lengths=input_len, strategy="greedy")[0][0]
# Convert the SparseTensor to a dense tensor
dense_results = tf.sparse.to_dense(results, default_value=-1)
# Slice the dense tensor to keep only up to max_length
dense_results = dense_results[:, :max_length]
# Iterate over the results and get back the text
output_text = []
for res in results:
for res in dense_results:
res = tf.strings.reduce_join(num_to_char(res)).numpy().decode("utf-8")
output_text.append(res)
return output_text
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