diff --git a/python/rapidocr/main.py b/python/rapidocr/main.py
index ceadbbc..a2890dd 100644
--- a/python/rapidocr/main.py
+++ b/python/rapidocr/main.py
@@ -23,9 +23,11 @@ from .utils.process_img import (
     apply_vertical_padding,
     get_rotate_crop_image,
     map_boxes_to_original,
+    map_img_to_original,
     resize_image_within_bounds,
 )
 from .utils.typings import LangRec
+from .utils.utils import filter_by_indices
 from .utils.vis_res import VisRes
 
 root_dir = Path(__file__).resolve().parent
@@ -159,28 +161,27 @@ class RapidOCR:
                 det_res.boxes, op_record, ori_h, ori_w
             )
 
+            ratio_h = op_record["preprocess"]["ratio_h"]
+            ratio_w = op_record["preprocess"]["ratio_w"]
+            cropped_img_list = map_img_to_original(cropped_img_list, ratio_h, ratio_w)
+
         # 过滤识别结果为空的值
         if (
             rec_res.txts is not None
             and det_res.boxes is not None
             and det_res.scores is not None
         ):
-            empty_ids = {i for i, v in enumerate(rec_res.txts) if not v.strip()}
+            valid_ids = [i for i, v in enumerate(rec_res.txts) if v.strip()]
 
-            det_res.boxes = np.array(
-                [v for i, v in enumerate(det_res.boxes) if i not in empty_ids]
-            )
-            det_res.scores = [
-                v for i, v in enumerate(det_res.scores) if i not in empty_ids
-            ]
+            det_res.boxes = filter_by_indices(det_res.boxes, valid_ids)
+            det_res.scores = filter_by_indices(det_res.scores, valid_ids)
 
-            rec_res.txts = [v for i, v in enumerate(rec_res.txts) if i not in empty_ids]
-            rec_res.word_results = [
-                v for i, v in enumerate(rec_res.word_results) if i not in empty_ids
-            ]
-            rec_res.scores = [
-                v for i, v in enumerate(rec_res.scores) if i not in empty_ids
-            ]
+            rec_res.imgs = filter_by_indices(rec_res.imgs, valid_ids)
+            rec_res.txts = filter_by_indices(rec_res.txts, valid_ids)
+            rec_res.word_results = filter_by_indices(rec_res.word_results, valid_ids)
+            rec_res.scores = filter_by_indices(rec_res.scores, valid_ids)
+
+            cropped_img_list = filter_by_indices(cropped_img_list, valid_ids)
 
         # 仅分类结果
         if (
@@ -243,17 +244,9 @@ class RapidOCR:
 
         origin_words = []
         for word_line in rec_res.word_results:
-            origin_words_item = []
-            for txt, score, bbox in word_line:
-                if bbox is None:
-                    continue
-
-                origin_words_points = map_boxes_to_original(
-                    np.array([bbox]).astype(np.float64), op_record, raw_h, raw_w
-                )
-                origin_words_points = origin_words_points.astype(np.int32).tolist()[0]
-                origin_words_item.append((txt, score, origin_words_points))
-
+            origin_words_item = [
+                (txt, score, bbox) for txt, score, bbox in word_line if bbox is not None
+            ]
             if origin_words_item:
                 origin_words.append(tuple(origin_words_item))
         return tuple(origin_words)
diff --git a/python/rapidocr/utils/process_img.py b/python/rapidocr/utils/process_img.py
index 46e8a16..13a2804 100644
--- a/python/rapidocr/utils/process_img.py
+++ b/python/rapidocr/utils/process_img.py
@@ -1,12 +1,24 @@
 # -*- encoding: utf-8 -*-
 # @Author: SWHL
 # @Contact: liekkaskono@163.com
-from typing import Any, Dict, Tuple
+from typing import Any, Dict, List, Tuple
 
 import cv2
 import numpy as np
 
 
+def map_img_to_original(
+    imgs: List[np.ndarray], ratio_h: float, ratio_w: float
+) -> List[np.ndarray]:
+    results = []
+    for img in imgs:
+        img_h, img_w = img.shape[:2]
+        ori_img_h, ori_img_w = round(img_h * ratio_h), round(img_w * ratio_w)
+        resize_img = cv2.resize(img, (ori_img_w, ori_img_h))
+        results.append(resize_img)
+    return results
+
+
 def map_boxes_to_original(
     dt_boxes: np.ndarray, op_record: Dict[str, Any], ori_h: int, ori_w: int
 ) -> np.ndarray:
diff --git a/python/rapidocr/utils/utils.py b/python/rapidocr/utils/utils.py
index c0f63a3..54ea641 100644
--- a/python/rapidocr/utils/utils.py
+++ b/python/rapidocr/utils/utils.py
@@ -5,13 +5,26 @@ import hashlib
 import importlib
 from pathlib import Path
 from sys import platform
-from typing import Tuple, Union
+from typing import Any, List, Tuple, Union
 from urllib.parse import urlparse
 
 import cv2
 import numpy as np
 
 
+def filter_by_indices(
+    data: Union[np.ndarray, List[Any], Tuple[Any]],
+    indices: Union[np.ndarray, List[int], Tuple[int, ...]],
+) -> Union[np.ndarray, List[Any], Tuple[Any]]:
+    if isinstance(data, np.ndarray):
+        return data[indices]
+
+    if isinstance(data, (list, tuple)):
+        return [data[i] for i in indices]
+
+    raise TypeError(f"Unsupported data type: {type(data)}")
+
+
 def mkdir(dir_path):
     Path(dir_path).mkdir(parents=True, exist_ok=True)
 
@@ -29,8 +42,16 @@ def quads_to_rect_bbox(bbox: np.ndarray) -> Tuple[float, float, float, float]:
     return float(x_min), float(y_min), float(x_max), float(y_max)
 
 
+def is_chinese_char(ch: str) -> bool:
+    return (
+        "\u4e00" <= ch <= "\u9fff"  # 汉字
+        or "\u3000" <= ch <= "\u303f"  # CJK 标点（如 。 、 “” 《》 ……）
+        or "\uff00" <= ch <= "\uffef"  # 全角符号（如 ，．！？【】）
+    )
+
+
 def has_chinese_char(text: str) -> bool:
-    return any("\u4e00" <= ch <= "\u9fff" for ch in text)
+    return any(is_chinese_char(ch) for ch in text)
 
 
 def get_file_sha256(file_path: Union[str, Path], chunk_size: int = 65536) -> str: