diff --git a/fluid/ocr_ctc/train.py b/fluid/ocr_ctc/train.py
new file mode 100644
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+++ b/fluid/ocr_ctc/train.py
@@ -0,0 +1,105 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+from __future__ import print_function
+
+import sys
+
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+
+
+def ocr_conv(input, num, with_bn):
+    assert (num % 4 == 0)
+
+    def conv_block(input, filter_size, group_size, with_bn):
+        return fluid.nets.img_conv_group(
+            input=input,
+            conv_num_filter=[num_filter] * groups,
+            pool_size=2,
+            pool_stride=2,
+            conv_padding=1,
+            conv_filter_size=3,
+            conv_act='relu',
+            conv_with_batchnorm=with_bn,
+            pool_type='max')
+
+    conv1 = conv_block(input, 16, (num / 4), with_bn)
+    conv2 = conv_block(conv1, 32, (num / 4), with_bn)
+    conv3 = conv_block(conv2, 64, (num / 4), with_bn)
+    conv4 = conv_block(conv3, 128, (num / 4), with_bn)
+    return conv4
+
+
+num_classes = 9054
+data_shape = [3, 32, 32]
+
+images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
+label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+# encoder part
+conv_features = ocr_convs(imges, 8, True)
+
+sliced_feature = fluid.layers.im2sequence(
+    input=conv_features,
+    stride_x=1,
+    stride_y=1,
+    block_x=1,
+    block_y=3, )
+
+gru_forward = fluid.layers.gru(input=sliced_feature, size=200, act="relu")
+gru_backward = fluid.layers.gru(input=sliced_feature,
+                                size=200,
+                                reverse=True,
+                                act="relu")
+
+out = fluid.layers.fc(input=[gru_forward, gru_backward], size=num_classes + 1)
+cost = fluid.layers.warpctc(
+    input=out,
+    label=label,
+    size=num_classes + 1,
+    blank=num_classes,
+    norm_by_times=True)
+
+optimizer = fluid.optimizer.Momentum(
+    learning_rate=((1.0e-3) / 16), momentum=0.9)
+opts = optimizer.minimize(cost)
+
+decoded_out = fluid.layers.ctc_greedy_decoder(input=output, blank=class_num)
+error = fluid.evaluator.EditDistance(input=decoded_out, label=label)
+
+BATCH_SIZE = 16
+PASS_NUM = 1
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.cifar.train10(), buf_size=128 * 10),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
+exe.run(fluid.default_startup_program())
+
+for pass_id in range(PASS_NUM):
+    accuracy.reset(exe)
+    for data in train_reader():
+        loss, acc = exe.run(fluid.default_main_program(),
+                            feed=feeder.feed(data),
+                            fetch_list=[avg_cost] + accuracy.metrics)
+        pass_acc = accuracy.eval(exe)
+        print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
+            pass_acc))
+        # this model is slow, so if we can train two mini batch, we think it works properly.
+        exit(0)
+exit(1)