[cherry-pick] Fix some typos from release/2.4 branch and merge. (#6114)

* [cherry-pick] Fix some typos from release/2.4 branch and merge.

* rm readme_en.md in deploy/cpp_infer/.

* Fix others typos.

notebook/notebook_ch/6.document_analysis/文档分析理论.ipynb

@@ -327,7 +327,7 @@
     "<img src=\"https://ai-studio-static-online.cdn.bcebos.com/899470ba601349fbbc402a4c83e6cdaee08aaa10b5004977b1f684f346ebe31f\" width=\"800\"/></center>\n",
     "<center>图 18: SER,RE任务示例</center>\n",
     "\n",
-    "一般的KIE方法基于命名实体识别(Named Entity Recognition,NER)[4]来研究，但是这类方法只利用了图像中的文本信息，缺少对视觉和结构信息的使用，因此精度不高。在此基础上，近几年的方法都开始将视觉和结构信息与文本信息融合到一起，按照对多模态信息进行融合时所采用的的原理可以将这些方法分为下面三种：\n",
+    "一般的KIE方法基于命名实体识别(Named Entity Recognition,NER)[4]来研究，但是这类方法只利用了图像中的文本信息，缺少对视觉和结构信息的使用，因此精度不高。在此基础上，近几年的方法都开始将视觉和结构信息与文本信息融合到一起，按照对多模态信息进行融合时所采用的原理可以将这些方法分为下面三种：\n",
     "\n",
     "1. 基于Grid的方法\n",
     "1. 基于Token的方法\n",

notebook/notebook_en/2.text_detection/text_detection_theory.ipynb

@@ -136,7 +136,7 @@
     "<br><center>Figure 11: LOMO frame diagram</center>\n",
     "\n",
     "\n",
-    "Contournet [18] is based on the proposed modeling of text contour points to obtain a curved text detection frame. This method first uses Adaptive-RPN to obtain the proposal features of the text area, and then designs a local orthogonal texture perception LOTM module to learn horizontal and vertical textures. The feature is represented by contour points. Finally, by considering the feature responses in two orthogonal directions at the same time, the Point Re-Scoring algorithm can effectively filter out the prediction of strong unidirectional or weak orthogonal activation, and the final text contour can be used as a A group of high-quality contour points are shown.\n",
+    "Contournet [18] is based on the proposed modeling of text contour points to obtain a curved text detection frame. This method first uses Adaptive-RPN to obtain the proposal features of the text area, and then designs a local orthogonal texture perception LOTM module to learn horizontal and vertical textures. The feature is represented by contour points. Finally, by considering the feature responses in two orthogonal directions at the same time, the Point Re-Scoring algorithm can effectively filter out the prediction of strong unidirectional or weak orthogonal activation, and the final text contour can be used as a group of high-quality contour points are shown.\n",
     "<center><img src=\"https://ai-studio-static-online.cdn.bcebos.com/1f59ab5db899412f8c70ba71e8dd31d4ea9480d6511f498ea492c97dd2152384\"\n",
     "width=\"600\" ></center>\n",
     "<br><center>Figure 12: Contournet frame diagram</center>\n",

ppstructure/vqa/tools/eval_with_label_end2end.py

@@ -82,7 +82,7 @@ def polygon_iou(poly1, poly2):
         except shapely.geos.TopologicalError:
             # except Exception as e:
             #     print(e)
-            print('shapely.geos.TopologicalError occured, iou set to 0')
+            print('shapely.geos.TopologicalError occurred, iou set to 0')
             iou = 0
     return iou
 

tools/end2end/eval_end2end.py

@@ -59,7 +59,7 @@ def polygon_iou(poly1, poly2):
         except shapely.geos.TopologicalError:
             # except Exception as e:
             #     print(e)
-            print('shapely.geos.TopologicalError occured, iou set to 0')
+            print('shapely.geos.TopologicalError occurred, iou set to 0')
             iou = 0
     return iou
 

tools/export_model.py

@@ -133,7 +133,7 @@ def main():
                 else:
                     config["Architecture"]["Models"][key]["Head"][
                         "out_channels"] = char_num
-                # just one final tensor needs to to exported for inference
+                # just one final tensor needs to exported for inference
                 config["Architecture"]["Models"][key][
                     "return_all_feats"] = False
         elif config['Architecture']['Head'][