OCR with CRNN. How to get prediction score - tensorflow

Question

I have a CRNN fitted model with CTC loss output.
I have the prediction and I use keras.backend.ctc_decode to decode it. As written in documentation (https://code.i-harness.com/en/docs/tensorflow~python/tf/keras/backend/ctc_decode), the function will return a Tuple with the decoded result and a Tensor with the log probability of the prediction.
keras.backend.ctc_decode can accept multiple values for its prediction but I need to pass it once at time.
This is the code:
def decode_single_prediction(pred, num_to_char):
input_len = np.ones(pred.shape[0]) * pred.shape[1]
# Use greedy search. For complex tasks, you can use beam search
decoded = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)
# decoded[0] is supposed to be the decoded result
# decoded[1] is supposed to be it's log probability
accuracy = float(decoded[1][0][0])
# take the resultin encoded char until it gets -1
result = decoded[0][0][:,: np.argmax(decoded[0][0] == -1)]
output_text = tf.strings.reduce_join(num_to_char(result)).numpy().decode("utf-8")
return (output_text, accuracy)
for image in images:
pred = prediction_model.predict(image)
# num_to_char is the mapping from number to char
pred_texts, acc = decode_single_prediction(pred, num_to_char)
print("True value: " + <true_result> + " prediction: " + pred_texts + " acc: " + str(acc))
Output:
True value: test0, prediction: test0, acc: 1.841524362564087
True value: test1, prediction: test1, acc: 0.9661365151405334
True value: test2, prediction: test2, acc: 1.0634151697158813
True value: test3, prediction: test3, acc: 2.471940755844116
True value: test4, prediction: test4, acc: 1.4866207838058472
True value: test5, prediction: test5, acc: 0.7630811333656311
True value: test6, prediction: test6, acc: 0.35642576217651367
True value: test7, prediction: test7, acc: 1.5693446397781372
True value: test8, prediction: test8, acc: 0.9700028896331787
True value: test9, prediction: test9, acc: 1.4783780574798584
The prediction is always correct. However what I think it's the probability seems not to be what I expect. They looks like completely random numbers, even grater than 1 or 2! What am I doing wrong??
Thank you in advance!