diff --git a/doc/doc_ch/recognition.md b/doc/doc_ch/recognition.md
index 0ff0513a2b9a3e5e732e78bd8b4f42ab9f79094f..3c79c03ec4e82147b0f235a4b204baa2d1ec92ad 100644
--- a/doc/doc_ch/recognition.md
+++ b/doc/doc_ch/recognition.md
@@ -185,11 +185,11 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs
 <a name="数据增强"></a>
 #### 2.1 数据增强
 
-PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。
+PaddleOCR提供了多种数据增强方式，默认配置文件中已经添加了数据增广。
 
-默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse)。
+默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse)、TIA数据增广。
 
-训练过程中每种扰动方式以50%的概率被选择，具体代码实现请参考：[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
+训练过程中每种扰动方式以40%的概率被选择，具体代码实现请参考：[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
 
 *由于OpenCV的兼容性问题，扰动操作暂时只支持Linux*
 
diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md
index 634ec783aa5e1dd6c9202385cf2978d140ca44a1..4a36db1e0a1940342d329fbbce2e79bf862dcefb 100644
--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
@@ -177,11 +177,11 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs
 <a name="Data_Augmentation"></a>
 #### 2.1 Data Augmentation
 
-PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file.
+PaddleOCR provides a variety of data augmentation methods. All the augmentation methods are enabled by default.
 
-The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse.
+The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, TIA augmentation.
 
-Each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to: [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
+Each disturbance method is selected with a 40% probability during the training process. For specific code implementation, please refer to: [rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
 
 <a name="Training"></a>
 #### 2.2 Training
diff --git a/ppocr/data/__init__.py b/ppocr/data/__init__.py
index e860c5a6986f495e6384d9df93c24795c04a0d5f..0bb3d506483a331fba48feafeff9ca2d439f3782 100644
--- a/ppocr/data/__init__.py
+++ b/ppocr/data/__init__.py
@@ -49,14 +49,12 @@ def term_mp(sig_num, frame):
     os.killpg(pgid, signal.SIGKILL)
 
 
-signal.signal(signal.SIGINT, term_mp)
-signal.signal(signal.SIGTERM, term_mp)
-
-
 def build_dataloader(config, mode, device, logger, seed=None):
     config = copy.deepcopy(config)
 
-    support_dict = ['SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet']
+    support_dict = [
+        'SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet'
+    ]
     module_name = config[mode]['dataset']['name']
     assert module_name in support_dict, Exception(
         'DataSet only support {}'.format(support_dict))
@@ -96,4 +94,8 @@ def build_dataloader(config, mode, device, logger, seed=None):
         return_list=True,
         use_shared_memory=use_shared_memory)
 
+    # support exit using ctrl+c
+    signal.signal(signal.SIGINT, term_mp)
+    signal.signal(signal.SIGTERM, term_mp)
+
     return data_loader
diff --git a/tools/infer/predict_e2e.py b/tools/infer/predict_e2e.py
index cd6c2005a7cc77c356e3f004cd586a84676ea7fa..5029d6059346a00062418d8d1b6cb029b0110643 100755
--- a/tools/infer/predict_e2e.py
+++ b/tools/infer/predict_e2e.py
@@ -74,7 +74,7 @@ class TextE2E(object):
 
         self.preprocess_op = create_operators(pre_process_list)
         self.postprocess_op = build_post_process(postprocess_params)
-        self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
+        self.predictor, self.input_tensor, self.output_tensors, _ = utility.create_predictor(
             args, 'e2e', logger)  # paddle.jit.load(args.det_model_dir)
         # self.predictor.eval()