diff --git a/paddlex/inference/pipelines/components/common/cal_ocr_word_box.py b/paddlex/inference/pipelines/components/common/cal_ocr_word_box.py
index f2f142bad5..4d4d254546 100644
--- a/paddlex/inference/pipelines/components/common/cal_ocr_word_box.py
+++ b/paddlex/inference/pipelines/components/common/cal_ocr_word_box.py
@@ -18,10 +18,19 @@
 
 # from .convert_points_and_boxes import convert_points_to_boxes
 
+def is_vertical_text(box: np.ndarray):
+    """Determine if the text box is vertical based on its aspect ratio."""
+
+    width = box[:,0].max() - box[:,0].min()
+    height = box[:,1].max() - box[:,1].min()
+    if (height / width) > 1.5:
+        return True
+    else:
+        return False
 
 def cal_ocr_word_box(rec_str, box, rec_word_info):
     """Calculate the detection frame for each word based on the results of recognition and detection of ocr"""
-
+    is_vertical = is_vertical_text(box)
     col_num, word_list, word_col_list, state_list = rec_word_info
     box = box.tolist()
     bbox_x_start = box[0][0]
@@ -29,50 +38,53 @@ def cal_ocr_word_box(rec_str, box, rec_word_info):
     bbox_y_start = box[0][1]
     bbox_y_end = box[2][1]
 
-    cell_width = (bbox_x_end - bbox_x_start) / col_num
+    if is_vertical:
+        cell_size = (bbox_y_end - bbox_y_start) / col_num
+        bbox_size = bbox_y_end - bbox_y_start
+        bbox_start = bbox_y_start
+        def create_box(start, end):
+            return (
+                    (bbox_x_start, start), (bbox_x_start, end),
+                    (bbox_x_end, end), (bbox_x_end, start)
+                )
+    else:
+        cell_size = (bbox_x_end - bbox_x_start) / col_num
+        bbox_size = bbox_x_end - bbox_x_start
+        bbox_start = bbox_x_start
+        def create_box(start, end):
+            return (
+                    (start, bbox_y_start), (end, bbox_y_start),
+                    (end, bbox_y_end), (start, bbox_y_end)
+                )
 
     word_box_list = []
     word_box_content_list = []
-    cn_width_list = []
+    cn_size_list = []
     cn_col_list = []
+
+    # Process words
     for word, word_col, state in zip(word_list, word_col_list, state_list):
         if state == "cn":
             if len(word_col) != 1:
-                char_seq_length = (word_col[-1] - word_col[0] + 1) * cell_width
-                char_width = char_seq_length / (len(word_col) - 1)
-                cn_width_list.append(char_width)
+                char_seq_length = (word_col[-1] - word_col[0] + 1) * cell_size
+                char_size = char_seq_length / (len(word_col) - 1)
+                cn_size_list.append(char_size)
             cn_col_list += word_col
             word_box_content_list += word
         else:
-            cell_x_start = bbox_x_start + int(word_col[0] * cell_width)
-            cell_x_end = bbox_x_start + int((word_col[-1] + 1) * cell_width)
-            cell = (
-                (cell_x_start, bbox_y_start),
-                (cell_x_end, bbox_y_start),
-                (cell_x_end, bbox_y_end),
-                (cell_x_start, bbox_y_end),
-            )
-            word_box_list.append(cell)
+            cell_start = bbox_start + int(word_col[0] * cell_size)
+            cell_end = bbox_start + int((word_col[-1] + 1) * cell_size)
+            word_box_list.append(create_box(cell_start, cell_end))
             word_box_content_list.append("".join(word))
+
     if len(cn_col_list) != 0:
-        if len(cn_width_list) != 0:
-            avg_char_width = np.mean(cn_width_list)
-        else:
-            avg_char_width = (bbox_x_end - bbox_x_start) / len(rec_str)
+        avg_char_size = np.mean(cn_size_list) if cn_size_list else bbox_size / len(rec_str)
         for center_idx in cn_col_list:
-            center_x = (center_idx + 0.5) * cell_width
-            cell_x_start = max(int(center_x - avg_char_width / 2), 0) + bbox_x_start
-            cell_x_end = (
-                min(int(center_x + avg_char_width / 2), bbox_x_end - bbox_x_start)
-                + bbox_x_start
-            )
-            cell = (
-                (cell_x_start, bbox_y_start),
-                (cell_x_end, bbox_y_start),
-                (cell_x_end, bbox_y_end),
-                (cell_x_start, bbox_y_end),
-            )
-            word_box_list.append(cell)
+            center = (center_idx + 0.5) * cell_size
+            cell_start = max(int(center - avg_char_size / 2), 0) + bbox_start
+            cell_end = min(int(center + avg_char_size / 2), bbox_size) + bbox_start
+            word_box_list.append(create_box(cell_start, cell_end))
+
     word_box_list = sort_boxes(word_box_list, y_thresh=12)
     return word_box_content_list, word_box_list