diff --git a/configs/rec/rec_mv3_tps_bilstm_attn.yml b/configs/rec/rec_mv3_tps_bilstm_attn.yml
index 792757b346eb457189b0f06d989c08b075abda4c..1787abc17029eeed2371a5a694ca6d0c94325b6e 100755
--- a/configs/rec/rec_mv3_tps_bilstm_attn.yml
+++ b/configs/rec/rec_mv3_tps_bilstm_attn.yml
@@ -12,8 +12,10 @@ Global:
   test_batch_size_per_card: 256
   image_shape: [3, 32, 100]
   max_text_length: 25
-  character_type: en
+  character_type: ch
+  character_dict_path: ./ppocr/utils/ppocr_keys_v1.txt
   loss_type: attention
+  tps: true
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
   pretrain_weights:
   checkpoints:
diff --git a/configs/rec/rec_mv3_tps_bilstm_ctc.yml b/configs/rec/rec_mv3_tps_bilstm_ctc.yml
index f15ddab7c4aa7f5c071f8f6cabed744bd8dcf61a..2892452b7539721a7a8d5279650b32832f441883 100755
--- a/configs/rec/rec_mv3_tps_bilstm_ctc.yml
+++ b/configs/rec/rec_mv3_tps_bilstm_ctc.yml
@@ -14,6 +14,7 @@ Global:
   max_text_length: 25
   character_type: en
   loss_type: ctc
+  tps: true
   reader_yml: ./configs/rec/rec_benchmark_reader.yml
   pretrain_weights:
   checkpoints:
diff --git a/ppocr/data/rec/dataset_traversal.py b/ppocr/data/rec/dataset_traversal.py
index 2429dd20dbc2ee75dc0d3f84e828c3754bc595cd..8d6b4d1e2f5cedcc0ce4760a351bfc4e4fe5a853 100755
--- a/ppocr/data/rec/dataset_traversal.py
+++ b/ppocr/data/rec/dataset_traversal.py
@@ -41,6 +41,8 @@ class LMDBReader(object):
         self.loss_type = params['loss_type']
         self.max_text_length = params['max_text_length']
         self.mode = params['mode']
+        if "tps" in params:
+            self.tps = True
         if params['mode'] == 'train':
             self.batch_size = params['train_batch_size_per_card']
             self.drop_last = params['drop_last']
@@ -109,7 +111,8 @@ class LMDBReader(object):
                     norm_img = process_image(
                         img=img,
                         image_shape=self.image_shape,
-                        char_ops=self.char_ops)
+                        char_ops=self.char_ops,
+                        tps=self.tps)
                     yield norm_img
             else:
                 lmdb_sets = self.load_hierarchical_lmdb_dataset()
diff --git a/ppocr/data/rec/img_tools.py b/ppocr/data/rec/img_tools.py
index 303e1fe31d54868685561ebbadb153e12350e75d..71efea1792d90cf02a8064190fbc956f2e7ef95d 100755
--- a/ppocr/data/rec/img_tools.py
+++ b/ppocr/data/rec/img_tools.py
@@ -92,11 +92,16 @@ def process_image(img,
                   label=None,
                   char_ops=None,
                   loss_type=None,
-                  max_text_length=None):
+                  max_text_length=None,
+                  tps=None):
     if char_ops.character_type == "en":
         norm_img = resize_norm_img(img, image_shape)
     else:
-        norm_img = resize_norm_img_chinese(img, image_shape)
+        if tps:
+            image_shape = [3, 32, 320]
+            norm_img = resize_norm_img(img, image_shape)
+        else:
+            norm_img = resize_norm_img_chinese(img, image_shape)
     norm_img = norm_img[np.newaxis, :]
     if label is not None:
         char_num = char_ops.get_char_num()
diff --git a/ppocr/modeling/architectures/rec_model.py b/ppocr/modeling/architectures/rec_model.py
index fdc0a641f0debc8ada6b00c193f3fc0ee255bb0e..d729f5d9218172356e1177c70e7f6bbff346c63b 100755
--- a/ppocr/modeling/architectures/rec_model.py
+++ b/ppocr/modeling/architectures/rec_model.py
@@ -30,6 +30,7 @@ class RecModel(object):
         global_params = params['Global']
         char_num = global_params['char_ops'].get_char_num()
         global_params['char_num'] = char_num
+        self.char_type = global_params['character_type']
         if "TPS" in params:
             tps_params = deepcopy(params["TPS"])
             tps_params.update(global_params)
@@ -60,8 +61,8 @@ class RecModel(object):
     def create_feed(self, mode):
         image_shape = deepcopy(self.image_shape)
         image_shape.insert(0, -1)
-        image = fluid.data(name='image', shape=image_shape, dtype='float32')
         if mode == "train":
+            image = fluid.data(name='image', shape=image_shape, dtype='float32')
             if self.loss_type == "attention":
                 label_in = fluid.data(
                     name='label_in',
@@ -86,6 +87,17 @@ class RecModel(object):
                 use_double_buffer=True,
                 iterable=False)
         else:
+            if self.char_type == "ch":
+                image_shape[-1] = -1
+                if self.tps != None:
+                    logger.info(
+                        "WARNRNG!!!\n"
+                        "TPS does not support variable shape in chinese!"
+                        "We set default shape=[3,32,320], it may affect the inference effect"
+                    )
+                    image_shape[-1] = 320
+                image = fluid.data(
+                    name='image', shape=image_shape, dtype='float32')
             labels = None
             loader = None
         return image, labels, loader
diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py
index 485531062b10c4710177b86d49fbfb9942d44477..914fcde31e5ea3efa544a0a2fa6a29266f835e5d 100755
--- a/tools/infer/predict_rec.py
+++ b/tools/infer/predict_rec.py
@@ -112,7 +112,7 @@ class TextRecognizer(object):
                     else:
                         preds = rec_idx_batch[rno, 1:end_pos[1]]
                         score = np.mean(predict_batch[rno, 1:end_pos[1]])
-                    #todo: why index has 2 offset
+                    #attenton index has 2 offset: beg and end
                     preds = preds - 2
                     preds_text = self.char_ops.decode(preds)
                     rec_res.append([preds_text, score])
@@ -138,7 +138,7 @@ if __name__ == "__main__":
     except:
         logger.info(
             "ERROR!! \nInput image shape is not equal with config. TPS does not support variable shape.\n"
-            "Please set --rec_image_shape=input_shape and --rec_char_type='ch' ")
+            "Please set --rec_image_shape=input_shape and --rec_char_type='en' ")
         exit()
     for ino in range(len(img_list)):
         print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))