code: def convert_annotations(root_path, split):
      """Convert original annotations to mmocr format.
  
      The annotation format is as the following:
          Crops/val/11/1/1.png weighted
          Crops/val/11/1/2.png 26
          Crops/val/11/1/3.png casting
          Crops/val/11/1/4.png 28
      After this module, the annotation has been changed to the format below:
          jsonl:
          {'filename': 'Crops/val/11/1/1.png', 'text': 'weighted'}
          {'filename': 'Crops/val/11/1/1.png', 'text': '26'}
          {'filename': 'Crops/val/11/1/1.png', 'text': 'casting'}
          {'filename': 'Crops/val/11/1/1.png', 'text': '28'}
  
      Args:
          root_path (str): The root path of the dataset
          split (str): The split of dataset. Namely: training or test
      """
      assert isinstance(root_path, str)
      assert isinstance(split, str)
  
      img_info = []
      with open(
          osp.join(root_path, f"{split}_label.txt"),
          encoding='"utf-8-sig',
      ) as f:
          annos = f.readlines()
      for anno in annos:
          if anno:
              # Text may contain spaces
              dst_img_name, word = anno.split("png ")
              word = word.strip("\n")
              img_info.append(
                  {
                      "file_name": dst_img_name + "png",
                      "anno_info": [{"text": word}],
                  }
              )
      dump_ocr_data(
          img_info,
          osp.join(root_path, f"{split.lower()}_label.json"),
          "textrecog",
      )
  
imports: import argparse
  import os.path
  from mmocr.utils import dump_ocr_data
label_desc: Use image node_id 1 for calling a global function with example usage: convert_annotations(root_path, split) without return types