diff --git a/tools/infer/predict_system.py b/tools/infer/predict_system.py
index 57f9a7e0a9c27436968219834ac14c815257596d..8d075502ea227369bfe8f804ffd9e1fd64888a74 100755
--- a/tools/infer/predict_system.py
+++ b/tools/infer/predict_system.py
@@ -13,9 +13,9 @@
 # limitations under the License.
 import os
 import sys
-__dir__ = os.path.dirname(__file__)
+__dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
-sys.path.append(os.path.join(__dir__, '../..'))
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
 
 import tools.infer.utility as utility
 from ppocr.utils.utility import initial_logger
@@ -39,6 +39,7 @@ class TextSystem(object):
         self.text_recognizer = predict_rec.TextRecognizer(args)
 
     def get_rotate_crop_image(self, img, points):
+        '''
         img_height, img_width = img.shape[0:2]
         left = int(np.min(points[:, 0]))
         right = int(np.max(points[:, 0]))
@@ -47,15 +48,19 @@ class TextSystem(object):
         img_crop = img[top:bottom, left:right, :].copy()
         points[:, 0] = points[:, 0] - left
         points[:, 1] = points[:, 1] - top
-        img_crop_width = int(np.linalg.norm(points[0] - points[1]))
-        img_crop_height = int(np.linalg.norm(points[0] - points[3]))
-        pts_std = np.float32([[0, 0], [img_crop_width, 0],\
-            [img_crop_width, img_crop_height], [0, img_crop_height]])
+        '''
+        img_crop_width = int(max(np.linalg.norm(points[0] - points[1]),
+                                 np.linalg.norm(points[2] - points[3])))
+        img_crop_height = int(max(np.linalg.norm(points[0] - points[3]),
+                                  np.linalg.norm(points[1] - points[2])))
+        pts_std = np.float32([[0, 0],
+                              [img_crop_width, 0],
+                              [img_crop_width, img_crop_height],
+                              [0, img_crop_height]])
         M = cv2.getPerspectiveTransform(points, pts_std)
-        dst_img = cv2.warpPerspective(
-            img_crop,
-            M, (img_crop_width, img_crop_height),
-            borderMode=cv2.BORDER_REPLICATE)
+        dst_img = cv2.warpPerspective(img, M, (img_crop_width, img_crop_height),
+                                      borderMode=cv2.BORDER_REPLICATE,
+                                      flags=cv2.INTER_CUBIC)
         dst_img_height, dst_img_width = dst_img.shape[0:2]
         if dst_img_height * 1.0 / dst_img_width >= 1.5:
             dst_img = np.rot90(dst_img)
@@ -106,8 +111,7 @@ def sorted_boxes(dt_boxes):
     return _boxes
 
 
-if __name__ == "__main__":
-    args = utility.parse_args()
+def main(args):
     image_file_list = get_image_file_list(args.image_dir)
     text_sys = TextSystem(args)
     is_visualize = True
@@ -145,3 +149,7 @@ if __name__ == "__main__":
                 draw_img[:, :, ::-1])
             print("The visualized image saved in {}".format(
                 os.path.join(draw_img_save, os.path.basename(image_file))))
+
+
+if __name__ == "__main__":
+    main(utility.parse_args())