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Dataset Migration

Based on the new design of BaseDataset in MMEngine, we have refactored the base OCR dataset class OCRDataset in MMOCR 1.0. The following document describes the differences between the old and new dataset formats in MMOCR, and how to migrate from the deprecated version to the latest. For users who do not want to migrate datasets at this time, we also provide a temporary solution in Section Compatibility.

Note

The Key Information Extraction task still uses the original WildReceipt dataset annotation format.

Review of Old Dataset Formats

MMOCR version 0.x implements a number of dataset classes, such as IcdarDataset, TextDetDataset for text detection tasks, and OCRDataset, OCRSegDataset for text recognition tasks. At the same time, the annotations may vary in different formats, such as .txt, .json, .jsonl. Users have to manually configure the Loader and the Parser while customizing the datasets.

Text Detection

For the text detection task, IcdarDataset uses a COCO-like annotation format.

{
  "images": [
    {
      "id": 1,
      "width": 800,
      "height": 600,
      "file_name": "test.jpg"
    }
  ],
  "annotations": [
    {
      "id": 1,
      "image_id": 1,
      "category_id": 1,
      "bbox": [0,0,10,10],
      "segmentation": [
          [0,0,10,0,10,10,0,10]
      ],
      "area": 100,
      "iscrowd": 0
    }
  ]
}

The TextDetDataset uses the JSON Line storage format, converting COCO-like labels to strings and saves them in .txt or .jsonl format files.

{"file_name": "test/img_2.jpg", "height": 720, "width": 1280,  "annotations": [{"iscrowd": 0, "category_id": 1, "bbox": [602.0, 173.0,  33.0, 24.0], "segmentation": [[602, 173, 635, 175, 634, 197, 602,  196]]}, {"iscrowd": 0, "category_id": 1, "bbox": [734.0, 310.0, 58.0,  54.0], "segmentation": [[734, 310, 792, 320, 792, 364, 738, 361]]}]}
{"file_name": "test/img_5.jpg", "height": 720, "width": 1280,  "annotations": [{"iscrowd": 1, "category_id": 1, "bbox": [405.0, 409.0,  32.0, 52.0], "segmentation": [[408, 409, 437, 436, 434, 461, 405,  433]]}, {"iscrowd": 1, "category_id": 1, "bbox": [435.0, 434.0, 8.0,  33.0], "segmentation": [[437, 434, 443, 440, 441, 467, 435, 462]]}]}

Text Recognition

For text recognition tasks, there are two annotation formats in MMOCR version 0.x. The simple .txt annotations separate image name and word annotation by a blank space, which cannot handle the case when spaces are included in a text instance.

img1.jpg OpenMMLab
img2.jpg MMOCR

The JSON Line format uses a dictionary-like structure to represent the annotations, where the keys filename and text store the image name and word label, respectively.

{"filename": "img1.jpg", "text": "OpenMMLab"}
{"filename": "img2.jpg", "text": "MMOCR"}

New Dataset Format

To solve the dataset issues, MMOCR 1.x adopts a unified dataset design introduced in MMEngine. Each annotation file is a .json file that stores a dict, containing both metainfo and data_list, where the former includes basic information about the dataset and the latter consists of the label item of each target instance.

{
  "metainfo":
    {
      "classes": ("cat", "dog"),
      // ...
    },
  "data_list":
    [
      {
        "img_path": "xxx/xxx_0.jpg",
        "img_label": 0,
        // ...
      },
      // ...
    ]
}

Based on the above structure, we introduced TextDetDataset, TextRecogDataset for MMOCR-specific tasks.

Text Detection

Introduction of the New Format

The TextDetDataset holds the information required by the text detection task, such as bounding boxes and labels. We refer users to tests/data/det_toy_dataset/instances_test.json which is an example annotation for TextDetDataset.

{
  "metainfo":
    {
      "dataset_type": "TextDetDataset",
      "task_name": "textdet",
      "category": [{"id": 0, "name": "text"}]
    },
  "data_list":
    [
      {
        "img_path": "test_img.jpg",
        "height": 640,
        "width": 640,
        "instances":
          [
            {
              "polygon": [0, 0, 0, 10, 10, 20, 20, 0],
              "bbox": [0, 0, 10, 20],
              "bbox_label": 0,
              "ignore": False
            },
            // ...
          ]
      }
    ]
}

Migration Script

We provide a migration script to help users migrate old annotation files to the new format.

python tools/dataset_converters/textdet/data_migrator.py ${IN_PATH} ${OUT_PATH}
ARGS Type Description
in_path str (Required)Path to the old annotation file.
out_path str (Required)Path to the new annotation file.
--task 'auto', 'textdet', 'textspotter' Specifies the compatible task for the output dataset annotation. If 'textdet' is specified, the text field in coco format will not be dumped. The default is 'auto', which automatically determines the output format based on the the old annotation files.

Text Recognition

Introduction of the New Format

The TextRecogDataset holds the information required by the text detection task, such as text and image path. We refer users to tests/data/rec_toy_dataset/labels.json which is an example annotation for TextRecogDataset.

{
  "metainfo":
    {
      "dataset_type": "TextRecogDataset",
      "task_name": "textrecog",
    },
    "data_list":
    [
      {
        "img_path": "test_img.jpg",
        "instances":
            [
              {
                "text": "GRAND"
              }
            ]
      }
    ]
}

Migration Script

We provide a migration script to help users migrate old annotation files to the new format.

python tools/dataset_converters/textrecog/data_migrator.py ${IN_PATH} ${OUT_PATH} --format ${txt, jsonl, lmdb}
ARGS Type Description
in_path str (Required)Path to the old annotation file.
out_path str (Required)Path to the new annotation file.
--format 'txt', 'jsonl', 'lmdb' Specify the format of the old dataset annotation.

Compatibility

In consideration of the cost to users for data migration, we have temporarily made MMOCR version 1.x compatible with the old MMOCR 0.x format.

Note

The code and components used for compatibility with the old data format may be completely removed in a future release. Therefore, we strongly recommend that users migrate their datasets to the new data format.

Specifically, we provide three dataset classes IcdarDataset, RecogTextDataset, RecogLMDBDataset to support the old formats.

  1. IcdarDataset supports COCO-like format annotations for text detection. You just need to add a new dataset config to configs/textdet/_base_/datasets and specify its dataset type as IcdarDataset.

    data_root = 'data/det/icdar2015'
    train_anno_path = 'instances_training.json'
    
    train_dataset = dict(
        type='IcdarDataset',
        data_root=data_root,
        ann_file=train_anno_path,
        data_prefix=dict(img_path='imgs/'),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=None)
    
  2. RecogTextDataset supports .txt and .jsonl format annotations for text recognition. You just need to add a new dataset config to configs/textrecog/_base_/datasets and specify its dataset type as RecogTextDataset. For example, the following example shows how to configure and load the 0.x format labels old_label.txt and old_label.jsonl from the toy dataset.

     data_root = 'tests/data/rec_toy_dataset/'
    
     # loading 0.x txt format annos
     txt_dataset = dict(
         type='RecogTextDataset',
         data_root=data_root,
         ann_file='old_label.txt',
         data_prefix=dict(img_path='imgs'),
         parser_cfg=dict(
             type='LineStrParser',
             keys=['filename', 'text'],
             keys_idx=[0, 1]),
         pipeline=[])
    
     # loading 0.x json line format annos
     jsonl_dataset = dict(
         type='RecogTextDataset',
         data_root=data_root,
         ann_file='old_label.jsonl',
         data_prefix=dict(img_path='imgs'),
         parser_cfg=dict(
             type='LineJsonParser',
             keys=['filename', 'text'],
         pipeline=[]))
    
  3. RecogLMDBDataset supports LMDB format dataset (img+labels) for text recognition. You just need to add a new dataset config to configs/textrecog/_base_/datasets and specify its dataset type as RecogLMDBDataset. For example, the following example shows how to configure and load the both labels and images imgs.lmdb from the toy dataset.

  • set the dataset type to RecogLMDBDataset

# Specify the dataset type as RecogLMDBDataset
 data_root = 'tests/data/rec_toy_dataset/'

 lmdb_dataset = dict(
     type='RecogLMDBDataset',
     data_root=data_root,
     ann_file='imgs.lmdb',
     pipeline=None)
  • replace the LoadImageFromFile with LoadImageFromNDArray in the data pipelines in train_pipeline and test_pipeline., for example:

 train_pipeline = [dict(type='LoadImageFromNDArray')]
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