# Dataset Types¶

## General Introduction¶

To support the tasks of text detection, text recognition and key information extraction, we have designed some new types of dataset which consist of loader and parser to load and parse different types of annotation files.

• loader: Load the annotation file. There are two types of loader, HardDiskLoader and LmdbLoader

• HardDiskLoader: Load txt format annotation file from hard disk to memory.

• LmdbLoader: Load lmdb format annotation file with lmdb backend, which is very useful for extremely large annotation files to avoid out-of-memory problem when ten or more GPUs are used, since each GPU will start multiple processes to load annotation file to memory.

• parser: Parse the annotation file line-by-line and return with dict format. There are two types of parser, LineStrParser and LineJsonParser.

• LineStrParser: Parse one line in ann file while treating it as a string and separating it to several parts by a separator. It can be used on tasks with simple annotation files such as text recognition where each line of the annotation files contains the filename and label attribute only.

• LineJsonParser: Parse one line in ann file while treating it as a json-string and using json.loads to convert it to dict. It can be used on tasks with complex annotation files such as text detection where each line of the annotation files contains multiple attributes (e.g. filename, height, width, box, segmentation, iscrowd, category_id, etc.).

Here we show some examples of using different combination of loader and parser.

### UniformConcatDataset¶

UniformConcatDataset is a dataset wrapper which allows users to apply a universal pipeline on multiple datasets without specifying the pipeline for each of them.

For example, to apply train_pipeline on both train1 and train2,

data = dict(
...
train=dict(
type='UniformConcatDataset',
datasets=[train1, train2],
pipeline=train_pipeline))


Also, it support apply different pipeline to different datasets,

train_list1 = [train1, train2]
train_list2 = [train3, train4]

data = dict(
...
train=dict(
type='UniformConcatDataset',
datasets=[train_list1, train_list2],
pipeline=[train_pipeline1, train_pipeline2]))


Here, train_pipeline1 will be applied to train1 and train2, and train_pipeline2 will be applied to train3 and train4.

### TextDetDataset¶

Dataset with annotation file in line-json txt format

dataset_type = 'TextDetDataset'
img_prefix = 'tests/data/toy_dataset/imgs'
test_anno_file = 'tests/data/toy_dataset/instances_test.txt'
test = dict(
type=dataset_type,
img_prefix=img_prefix,
ann_file=test_anno_file,
repeat=4,
parser=dict(
type='LineJsonParser',
keys=['file_name', 'height', 'width', 'annotations'])),
pipeline=test_pipeline,
test_mode=True)


The results are generated in the same way as the segmentation-based text recognition task above. You can check the content of the annotation file in tests/data/toy_dataset/instances_test.txt. The combination of HardDiskLoader and LineJsonParser will return a dict for each file by calling __getitem__:

{"file_name": "test/img_10.jpg", "height": 720, "width": 1280, "annotations": [{"iscrowd": 1, "category_id": 1, "bbox": [260.0, 138.0, 24.0, 20.0], "segmentation": [[261, 138, 284, 140, 279, 158, 260, 158]]}, {"iscrowd": 0, "category_id": 1, "bbox": [288.0, 138.0, 129.0, 23.0], "segmentation": [[288, 138, 417, 140, 416, 161, 290, 157]]}, {"iscrowd": 0, "category_id": 1, "bbox": [743.0, 145.0, 37.0, 18.0], "segmentation": [[743, 145, 779, 146, 780, 163, 746, 163]]}, {"iscrowd": 0, "category_id": 1, "bbox": [783.0, 129.0, 50.0, 26.0], "segmentation": [[783, 129, 831, 132, 833, 155, 785, 153]]}, {"iscrowd": 1, "category_id": 1, "bbox": [831.0, 133.0, 43.0, 23.0], "segmentation": [[831, 133, 870, 135, 874, 156, 835, 155]]}, {"iscrowd": 1, "category_id": 1, "bbox": [159.0, 204.0, 72.0, 15.0], "segmentation": [[159, 205, 230, 204, 231, 218, 159, 219]]}, {"iscrowd": 1, "category_id": 1, "bbox": [785.0, 158.0, 75.0, 21.0], "segmentation": [[785, 158, 856, 158, 860, 178, 787, 179]]}, {"iscrowd": 1, "category_id": 1, "bbox": [1011.0, 157.0, 68.0, 16.0], "segmentation": [[1011, 157, 1079, 160, 1076, 173, 1011, 170]]}]}


### IcdarDataset¶

Dataset with annotation file in coco-like json format

For text detection, you can also use an annotation file in a COCO format that is defined in MMDetection:

dataset_type = 'IcdarDataset'
prefix = 'tests/data/toy_dataset/'
test=dict(
type=dataset_type,
ann_file=prefix + 'instances_test.json',
img_prefix=prefix + 'imgs',
pipeline=test_pipeline)


You can check the content of the annotation file in tests/data/toy_dataset/instances_test.json.

Note

Icdar 2015/2017 and ctw1500 annotations need to be converted into the COCO format following the steps in datasets.md.

### OCRDataset¶

Dataset for encoder-decoder based recognizer

dataset_type = 'OCRDataset'
img_prefix = 'tests/data/ocr_toy_dataset/imgs'
train_anno_file = 'tests/data/ocr_toy_dataset/label.txt'
train = dict(
type=dataset_type,
img_prefix=img_prefix,
ann_file=train_anno_file,
repeat=10,
parser=dict(
type='LineStrParser',
keys=['filename', 'text'],
keys_idx=[0, 1],
separator=' ')),
pipeline=train_pipeline,
test_mode=False)


You can check the content of the annotation file in tests/data/ocr_toy_dataset/label.txt. The combination of HardDiskLoader and LineStrParser will return a dict for each file by calling __getitem__: {'filename': '1223731.jpg', 'text': 'GRAND'}.

Optional Arguments:

• repeat: The number of repeated lines in the annotation files. For example, if there are 10 lines in the annotation file, setting repeat=10 will generate a corresponding annotation file with size 100.

If the annotation file is extremely large, you can convert it from txt format to lmdb format with the following command:

python tools/data_converter/txt2lmdb.py -i ann_file.txt -o ann_file.lmdb


After that, you can use LmdbLoader in dataset like below.

img_prefix = 'tests/data/ocr_toy_dataset/imgs'
train_anno_file = 'tests/data/ocr_toy_dataset/label.lmdb'
train = dict(
type=dataset_type,
img_prefix=img_prefix,
ann_file=train_anno_file,
repeat=10,
parser=dict(
type='LineStrParser',
keys=['filename', 'text'],
keys_idx=[0, 1],
separator=' ')),
pipeline=train_pipeline,
test_mode=False)


### OCRSegDataset¶

Dataset for segmentation-based recognizer

prefix = 'tests/data/ocr_char_ann_toy_dataset/'
train = dict(
type='OCRSegDataset',
img_prefix=prefix + 'imgs',
ann_file=prefix + 'instances_train.txt',

You can check the content of the annotation file in tests/data/ocr_char_ann_toy_dataset/instances_train.txt. The combination of HardDiskLoader and LineJsonParser will return a dict for each file by calling __getitem__ each time:
{"file_name": "resort_88_101_1.png", "annotations": [{"char_text": "F", "char_box": [11.0, 0.0, 22.0, 0.0, 12.0, 12.0, 0.0, 12.0]}, {"char_text": "r", "char_box": [23.0, 2.0, 31.0, 1.0, 24.0, 11.0, 16.0, 11.0]}, {"char_text": "o", "char_box": [33.0, 2.0, 43.0, 2.0, 36.0, 12.0, 25.0, 12.0]}, {"char_text": "m", "char_box": [46.0, 2.0, 61.0, 2.0, 53.0, 12.0, 39.0, 12.0]}, {"char_text": ":", "char_box": [61.0, 2.0, 69.0, 2.0, 63.0, 12.0, 55.0, 12.0]}], "text": "From:"}