diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py
index 1989f524c21d519f879290bcc72bc1b282a93772..7c94225506bb2d3291503e033fd9139fc2b752a4 100755
--- a/tools/infer/predict_rec.py
+++ b/tools/infer/predict_rec.py
@@ -60,9 +60,10 @@ class TextRecognizer(object):
             self.loss_type = 'srn'
         self.char_ops = CharacterOps(char_ops_params)
 
-    def resize_norm_img(self, img, max_wh_ratio):
+    def resize_norm_img(self, img, wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
         assert imgC == img.shape[2]
+        max_wh_ration = max(wh_ratio, imgW * 1.0 / imgH)
         if self.character_type == "ch":
             imgW = int((32 * max_wh_ratio))
         h, w = img.shape[:2]
@@ -173,16 +174,14 @@ class TextRecognizer(object):
         for beg_img_no in range(0, img_num, batch_num):
             end_img_no = min(img_num, beg_img_no + batch_num)
             norm_img_batch = []
-            max_wh_ratio = 0
             for ino in range(beg_img_no, end_img_no):
                 # h, w = img_list[ino].shape[0:2]
                 h, w = img_list[indices[ino]].shape[0:2]
                 wh_ratio = w * 1.0 / h
-                max_wh_ratio = max(max_wh_ratio, wh_ratio)
             for ino in range(beg_img_no, end_img_no):
                 if self.loss_type != "srn":
                     norm_img = self.resize_norm_img(img_list[indices[ino]],
-                                                    max_wh_ratio)
+                                                    wh_ratio)
                     norm_img = norm_img[np.newaxis, :]
                     norm_img_batch.append(norm_img)
                 else: