def METHOD_NAME(root_path,
                 split,
                 ratio,
                 preserve_vertical,
                 nproc,
                 img_start_idx=0):
    """Collect the annotation information and crop the images.
    The annotation format is as the following:
    [
        {'gt_1234': # 'gt_1234' is file name
            [
                {
                    'transcription': '一站式购物中心',
                    'points': [[45, 272], [215, 273], [212, 296], [45, 290]]
                    'illegibility': False
                }, ...
            ]
        }
    ]
    Args:
        root_path (str): The root path of the dataset
        split (str): The split of dataset. Namely: training or val
        ratio (float): Split ratio for val set
        preserve_vertical (bool): Whether to preserve vertical texts
        nproc (int): The number of process to collect annotations
        img_start_idx (int): Index of start image
    Returns:
        img_info (dict): The dict of the img and annotation information
    """
    annotation_path = osp.join(root_path, 'annotations/train_full_labels.json')
    if not osp.exists(annotation_path):
        raise Exception(
            f'{annotation_path} not exists, please check and try again.')
    annotation = mmengine.load(annotation_path)
    # outputs
    dst_label_file = osp.join(root_path, f'{split}_label.json')
    dst_image_root = osp.join(root_path, 'crops', split)
    ignore_image_root = osp.join(root_path, 'ignores', split)
    src_image_root = osp.join(root_path, 'imgs')
    mmengine.mkdir_or_exist(dst_image_root)
    mmengine.mkdir_or_exist(ignore_image_root)
    process_img_with_path = partial(
        process_img,
        dst_image_root=dst_image_root,
        ignore_image_root=ignore_image_root,
        preserve_vertical=preserve_vertical,
        split=split)
    img_prefixes = annotation.keys()
    trn_files, val_files = [], []
    if ratio > 0:
        for i, file in enumerate(img_prefixes):
            if i % math.floor(1 / ratio):
                trn_files.append(file)
            else:
                val_files.append(file)
    else:
        trn_files, val_files = img_prefixes, []
    print(f'training #{len(trn_files)}, val #{len(val_files)}')
    if split == 'train':
        img_prefixes = trn_files
    elif split == 'val':
        img_prefixes = val_files
    else:
        raise NotImplementedError
    tasks = []
    idx = 0
    for img_idx, prefix in enumerate(img_prefixes):
        img_file = osp.join(src_image_root, prefix + '.jpg')
        img_info = {'file_name': img_file}
        # Skip not exist images
        if not osp.exists(img_file):
            continue
        tasks.append((img_idx + img_start_idx, img_info, annotation[prefix]))
        idx = idx + 1
    labels_list = mmengine.track_parallel_progress(
        process_img_with_path, tasks, keep_order=True, nproc=nproc)
    final_labels = []
    for label_list in labels_list:
        final_labels += label_list
    dump_ocr_data(final_labels, dst_label_file, 'textrecog')
    return idx