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mmocr/docs/en/user_guides/dataset_prepare.md

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Dataset Preparation

Introduction

After decades of development, the OCR community has produced a series of related datasets that often provide annotations of text in a variety of styles, making it necessary for users to convert these datasets to the required format when using them. MMOCR supports dozens of commonly used text-related datasets and provides detailed tutorials for downloading and preparing the data.

In addition, we provide data conversion scripts to help users convert the annotations of widely-used OCR datasets to MMOCR formats.

In the following, we provide a brief overview of the data formats defined in MMOCR for each task.

  • As shown in the following code block, the text detection task uses the data format TextDetDataset, which holds the bounding box annotations, file names, and other information required for the text detection task. We provide a sample annotation file in the tests/data/det_toy_dataset/instances_test.json path.

    {
  "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,
            },
          ],
          //...
      }
    ]
}

  • As shown in the following code block, the text recognition task uses the data format TextRecogDataset, which holds information such as text instances and image paths required by the text recognition task. An example annotation file is provided in the tests/data/rec_toy_dataset/labels.json path.

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

Downloading Datasets and Format Conversion

As an example of the data preparation steps, you can perform the following steps to prepare the ICDAR 2015 dataset for text detection task.

  • Download the ICDAR 2015 dataset from the official ICDAR website. Extract the training set ch4_training_word_images_gt.zip and the test set zip ch4_test_word_images_gt.zip to the path data/icdar2015 respectively.

    # Downloading datasets
mkdir data/det/icdar2015 && cd data/det/icdar2015
wget https://rrc.cvc.uab.es/downloads/ch4_training_images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/ch4_training_localization_transcription_gt.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/ch4_test_images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/Challenge4_Test_Task1_GT.zip --no-check-certificate

# Extracting the zips
mkdir imgs && mkdir annotations
unzip ch4_training_images.zip -d imgs/training
unzip ch4_training_localization_transcription_gt.zip -d annotations/training
unzip ch4_test_images.zip -d imgs/test
unzip Challenge4_Test_Task1_GT.zip -d annotations/test

  • Using the scripts provided by us to convert the annotations to MMOCR supported formats.

    python tools/dataset_converters/textdet/icdar_converter.py data/det/icdar15/ -o data/det/icdar15/ --split-list training test -d icdar2015

  • After completing the above steps, the annotation format has been converted, and the file directory structure is as follows

    data/det/icdar2015/
├── annotations
│   ├── test
│   └── training
├── imgs
│   ├── test
│   └── training
├── instances_test.json
└── instances_training.json

Dataset Configuration

Single Dataset Training

When training or evaluating a model on new datasets, we need to write the dataset config where the image path, annotation path, and image prefix are set. The path configs/xxx/_base_/datasets/ is pre-configured with the commonly used datasets in MMOCR, here we take the ICDAR 2015 dataset as an example (see configs/_base_/det_datasets/icdar2015.py).

ic15_det_data_root = 'data/det/icdar2015' # dataset root path

# Train set config
ic15_det_train = dict(
    type='OCRDataset',
    data_root=ic15_det_data_root,                        # dataset root path
    ann_file='instances_training.json',                  # name of annotation
    data_prefix=dict(img_path='imgs/'),                  # prefix of image path
    filter_cfg=dict(filter_empty_gt=True, min_size=32),  # filtering empty images
    pipeline=None)
# Test set config
ic15_det_test = dict(
    type='OCRDataset',
    data_root=ic15_det_data_root,
    ann_file='instances_test.json',
    data_prefix=dict(img_path='imgs/'),
    test_mode=True,
    pipeline=None)

After configuring the dataset, we can import it in the corresponding model configs. For example, to train the "DBNet_R18" model on the ICDAR 2015 dataset.

_base_ = [
    '_base_dbnet_r18_fpnc.py',
    '../_base_/datasets/icdar2015.py',  # import the dataset config
    '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_sgd_1200e.py',
]

ic15_det_train = _base_.ic15_det_train            # specify the training set
ic15_det_train.pipeline = _base_.train_pipeline   # specify the training pipeline
ic15_det_test = _base_.ic15_det_test              # specify the testing set
ic15_det_test.pipeline = _base_.test_pipeline     # specify the testing pipeline

train_dataloader = dict(
    batch_size=16,
    num_workers=8,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=ic15_det_train)    # specify the dataset in train_dataloader

val_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=ic15_det_test)    # specify the dataset in val_dataloader

test_dataloader = val_dataloader

Multi-dataset Training

In addition, ConcatDataset enables users to train or test the model on a combination of multiple datasets. You just need to set the dataset type in the dataloader to ConcatDataset in the configuration file and specify the corresponding list of datasets.

train_list = [ic11, ic13, ic15]
train_dataloader = dict(
    dataset=dict(
        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))

For example, the following configuration uses the MJSynth dataset for training and 6 academic datasets (CUTE80, IIIT5K, SVT, SVTP, ICDAR2013, ICDAR2015) for testing.

_base_ = [ # Import all dataset configurations you want to use
    '../_base_/datasets/mjsynth.py',
    '../_base_/datasets/cute80.py',
    '../_base_/datasets/iiit5k.py',
    '../_base_/datasets/svt.py',
    '../_base_/datasets/svtp.py',
    '../_base_/datasets/icdar2013.py',
    '../_base_/datasets/icdar2015.py',
    '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_adadelta_5e.py',
    '_base_crnn_mini-vgg.py',
]

# List of training datasets
train_list = [_base_.mj_rec_train]
# List of testing datasets
test_list = [
    _base_.cute80_rec_test, _base_.iiit5k_rec_test, _base_.svt_rec_test,
    _base_.svtp_rec_test, _base_.ic13_rec_test, _base_.ic15_rec_test
]

# Use ConcatDataset to combine the datasets in the list
train_dataset = dict(
       type='ConcatDataset', datasets=train_list, pipeline=_base_.train_pipeline)
test_dataset = dict(
       type='ConcatDataset', datasets=test_list, pipeline=_base_.test_pipeline)

train_dataloader = dict(
    batch_size=192 * 4,
    num_workers=32,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=train_dataset)

test_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=test_dataset)

val_dataloader = test_dataloader