diff --git a/examples/vision/captcha_ocr.py b/examples/vision/captcha_ocr.py
index a6bac599ff..90e9ba214b 100644
--- a/examples/vision/captcha_ocr.py
+++ b/examples/vision/captcha_ocr.py
@@ -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
@@ -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