diff --git a/tools/data/textdet/ic11_converter.py b/tools/data/textdet/ic11_converter.py
new file mode 100644
index 00000000..84f0099b
--- /dev/null
+++ b/tools/data/textdet/ic11_converter.py
@@ -0,0 +1,172 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+import mmcv
+from PIL import Image
+
+from mmocr.utils import convert_annotations
+
+
+def convert_gif(img_path):
+    """Convert the gif image to png format.
+
+    Args:
+        img_path (str): The path to the gif image
+    """
+    img = Image.open(img_path)
+    dst_path = img_path.replace('gif', 'png')
+    img.save(dst_path)
+    os.remove(img_path)
+    print(f'Convert {img_path} to {dst_path}')
+
+
+def collect_files(img_dir, gt_dir):
+    """Collect all images and their corresponding groundtruth files.
+
+    Args:
+        img_dir (str): The image directory
+        gt_dir (str): The groundtruth directory
+
+    Returns:
+        files (list): The list of tuples (img_file, groundtruth_file)
+    """
+    assert isinstance(img_dir, str)
+    assert img_dir
+    assert isinstance(gt_dir, str)
+    assert gt_dir
+
+    ann_list, imgs_list = [], []
+    for img in os.listdir(img_dir):
+        img_path = osp.join(img_dir, img)
+        # mmcv cannot read gif images, so convert them to png
+        if img.endswith('gif'):
+            convert_gif(img_path)
+            img_path = img_path.replace('gif', 'png')
+        imgs_list.append(img_path)
+        ann_list.append(osp.join(gt_dir, 'gt_' + img.split('.')[0] + '.txt'))
+
+    files = list(zip(sorted(imgs_list), sorted(ann_list)))
+    assert len(files), f'No images found in {img_dir}'
+    print(f'Loaded {len(files)} images from {img_dir}')
+
+    return files
+
+
+def collect_annotations(files, nproc=1):
+    """Collect the annotation information.
+
+    Args:
+        files (list): The list of tuples (image_file, groundtruth_file)
+        nproc (int): The number of process to collect annotations
+
+    Returns:
+        images (list): The list of image information dicts
+    """
+    assert isinstance(files, list)
+    assert isinstance(nproc, int)
+
+    if nproc > 1:
+        images = mmcv.track_parallel_progress(
+            load_img_info, files, nproc=nproc)
+    else:
+        images = mmcv.track_progress(load_img_info, files)
+
+    return images
+
+
+def load_img_info(files):
+    """Load the information of one image.
+
+    Args:
+        files (tuple): The tuple of (img_file, groundtruth_file)
+
+    Returns:
+        img_info (dict): The dict of the img and annotation information
+    """
+    assert isinstance(files, tuple)
+
+    img_file, gt_file = files
+    # read imgs while ignoring orientations
+    img = mmcv.imread(img_file, 'unchanged')
+
+    img_info = dict(
+        file_name=osp.join(osp.basename(img_file)),
+        height=img.shape[0],
+        width=img.shape[1],
+        segm_file=osp.join(osp.basename(gt_file)))
+
+    if osp.splitext(gt_file)[1] == '.txt':
+        img_info = load_txt_info(gt_file, img_info)
+    else:
+        raise NotImplementedError
+
+    return img_info
+
+
+def load_txt_info(gt_file, img_info):
+    """Collect the annotation information.
+
+    The annotation format is as the following:
+    left, top, right, bottom, "transcription"
+
+    Args:
+        gt_file (str): The path to ground-truth
+        img_info (dict): The dict of the img and annotation information
+
+    Returns:
+        img_info (dict): The dict of the img and annotation information
+    """
+    anno_info = []
+    with open(gt_file, 'r') as f:
+        lines = f.readlines()
+        for line in lines:
+            xmin, ymin, xmax, ymax = line.split(',')[0:4]
+            x = max(0, int(xmin))
+            y = max(0, int(ymin))
+            w = int(xmax) - x
+            h = int(ymax) - y
+            bbox = [x, y, w, h]
+            segmentation = [x, y, x + w, y, x + w, y + h, x, y + h]
+
+            anno = dict(
+                iscrowd=0,
+                category_id=1,
+                bbox=bbox,
+                area=w * h,
+                segmentation=[segmentation])
+            anno_info.append(anno)
+    img_info.update(anno_info=anno_info)
+
+    return img_info
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Generate training and test set of IC11')
+    parser.add_argument('root_path', help='Root dir path of IC11')
+    parser.add_argument(
+        '--nproc', default=1, type=int, help='Number of process')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    root_path = args.root_path
+
+    for split in ['training', 'test']:
+        print(f'Processing {split} set...')
+        with mmcv.Timer(print_tmpl='It takes {}s to convert annotation'):
+            files = collect_files(
+                osp.join(root_path, 'imgs', split),
+                osp.join(root_path, 'annotations', split))
+            image_infos = collect_annotations(files, nproc=args.nproc)
+            convert_annotations(
+                image_infos, osp.join(root_path,
+                                      'instances_' + split + '.json'))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/data/textrecog/ic11_converter.py b/tools/data/textrecog/ic11_converter.py
new file mode 100644
index 00000000..e46b395a
--- /dev/null
+++ b/tools/data/textrecog/ic11_converter.py
@@ -0,0 +1,87 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import json
+import os.path as osp
+
+from mmocr.utils.fileio import list_to_file
+
+
+def convert_annotations(root_path, split, format):
+    """Convert original annotations to mmocr format.
+
+    The annotation format is as the following:
+        word_1.png, "flying"
+        word_2.png, "today"
+        word_3.png, "means"
+    After this module, the annotation has been changed to the format below:
+        txt:
+        word_1.png flying
+        word_2.png today
+        word_3.png means
+
+        jsonl:
+        {'filename': 'word_1.png', 'text': 'flying'}
+        {'filename': 'word_2.png', 'text': 'today'}
+        {'filename': 'word_3.png', 'text': 'means'}
+
+    Args:
+        root_path (str): The root path of the dataset
+        split (str): The split of dataset. Namely: Train or Test
+        format (str): Annotation format, should be either 'txt' or 'jsonl'
+    """
+    assert isinstance(root_path, str)
+    assert isinstance(split, str)
+
+    lines = []
+    with open(
+            osp.join(root_path, 'annotations',
+                     f'Challenge1_{split}_Task3_GT.txt'),
+            'r',
+            encoding='"utf-8-sig') as f:
+        annos = f.readlines()
+    dst_image_root = osp.join(root_path, split)
+    for anno in annos:
+        # text may contain comma ','
+        dst_img_name, word = anno.split(', "')
+        word = word.replace('"\n', '')
+
+        if format == 'txt':
+            lines.append(f'{osp.basename(dst_image_root)}/{dst_img_name} '
+                         f'{word}')
+        elif format == 'jsonl':
+            lines.append(
+                json.dumps({
+                    'filename':
+                    f'{osp.basename(dst_image_root)}/{dst_img_name}',
+                    'text': word
+                }))
+        else:
+            raise NotImplementedError
+
+    list_to_file(osp.join(root_path, f'{split}_label.{format}'), lines)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Generate training and test set of IC11')
+    parser.add_argument('root_path', help='Root dir path of IC11')
+    parser.add_argument(
+        '--format',
+        default='jsonl',
+        help='Use jsonl or string to format annotations',
+        choices=['jsonl', 'txt'])
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    root_path = args.root_path
+
+    for split in ['Train', 'Test']:
+        convert_annotations(root_path, split, args.format)
+        print(f'{split} split converted.')
+
+
+if __name__ == '__main__':
+    main()