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Datasets Preparation

This page lists the datasets which are commonly used in text detection, text recognition and key information extraction, and their download links.

Text Detection

The structure of the text detection dataset directory is organized as follows.

├── ctw1500
│   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
├── icdar2015
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
├── icdar2017
│   ├── imgs
│   ├── instances_training.json
│   └── instances_val.json
├── synthtext
│   ├── imgs
│   └── instances_training.lmdb
├── textocr
│   ├── train
│   ├── instances_training.json
│   └── instances_val.json
├── totaltext
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
Dataset Images Annotation Files
training validation testing
CTW1500 homepage - - -
ICDAR2015 homepage instances_training.json - instances_test.json
ICDAR2017 homepage instances_training.json instances_val.json -
Synthtext homepage instances_training.lmdb - -
TextOCR homepage - - -
Totaltext homepage - - -

Note: For users who want to train models on CTW1500, ICDAR 2015/2017, and Totaltext dataset, there might be some images containing orientation info in EXIF data. The default OpenCV backend used in MMCV would read them and apply the rotation on the images. However, their gold annotations are made on the raw pixels, and such inconsistency results in false examples in the training set. Therefore, users should use dict(type='LoadImageFromFile', color_type='color_ignore_orientation') in pipelines to change MMCV's default loading behaviour. (see DBNet's config for example)

  • For icdar2015:

    • Step1: Download ch4_training_images.zip, ch4_test_images.zip, ch4_training_localization_transcription_gt.zip, Challenge4_Test_Task1_GT.zip from homepage
    • Step2:
    mkdir icdar2015 && cd icdar2015
mkdir imgs && mkdir annotations
# For images,
mv ch4_training_images imgs/training
mv ch4_test_images imgs/test
# For annotations,
mv ch4_training_localization_transcription_gt annotations/training
mv Challenge4_Test_Task1_GT annotations/test
    python tools/data/textdet/icdar_converter.py /path/to/icdar2015 -o /path/to/icdar2015 -d icdar2015 --split-list training test

  • For icdar2017:

    • Follow similar steps as above.

  • For ctw1500:

    • Step1: Download train_images.zip, test_images.zip, train_labels.zip, test_labels.zip from github
    mkdir ctw1500 && cd ctw1500
mkdir imgs && mkdir annotations

# For annotations
cd annotations
wget -O train_labels.zip https://universityofadelaide.box.com/shared/static/jikuazluzyj4lq6umzei7m2ppmt3afyw.zip
wget -O test_labels.zip https://cloudstor.aarnet.edu.au/plus/s/uoeFl0pCN9BOCN5/download
unzip train_labels.zip && mv ctw1500_train_labels training
unzip test_labels.zip -d test
cd ..
# For images
cd imgs
wget -O train_images.zip https://universityofadelaide.box.com/shared/static/py5uwlfyyytbb2pxzq9czvu6fuqbjdh8.zip
wget -O test_images.zip https://universityofadelaide.box.com/shared/static/t4w48ofnqkdw7jyc4t11nsukoeqk9c3d.zip
unzip train_images.zip && mv train_images training
unzip test_images.zip && mv test_images test
    • Step2: Generate instances_training.json and instances_test.json with following command:
    python tools/data/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1500 --split-list training test

  • For TextOCR:

    mkdir textocr && cd textocr

# Download TextOCR dataset
wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip
wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_train.json
wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_val.json

# For images
unzip -q train_val_images.zip
mv train_images train
    • Step2: Generate instances_training.json and instances_val.json with the following command:
    python tools/data/textdet/textocr_converter.py /path/to/textocr

  • For Totaltext:

    • Step1: Download totaltext.zip from github dataset and groundtruth_text.zip from github Groundtruth (Our totaltext_converter.py supports groundtruth with both .mat and .txt format).
    mkdir totaltext && cd totaltext
mkdir imgs && mkdir annotations

# For images
# in ./totaltext
unzip totaltext.zip
mv Images/Train imgs/training
mv Images/Test imgs/test

# For annotations
unzip groundtruth_text.zip
cd Groundtruth
mv Polygon/Train ../annotations/training
mv Polygon/Test ../annotations/test
    • Step2: Generate instances_training.json and instances_test.json with the following command:
    python tools/data/textdet/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test

Text Recognition

The structure of the text recognition dataset directory is organized as follows.

├── mixture
│   ├── coco_text
│   │   ├── train_label.txt
│   │   ├── train_words
│   ├── icdar_2011
│   │   ├── training_label.txt
│   │   ├── Challenge1_Training_Task3_Images_GT
│   ├── icdar_2013
│   │   ├── train_label.txt
│   │   ├── test_label_1015.txt
│   │   ├── test_label_1095.txt
│   │   ├── Challenge2_Training_Task3_Images_GT
│   │   ├── Challenge2_Test_Task3_Images
│   ├── icdar_2015
│   │   ├── train_label.txt
│   │   ├── test_label.txt
│   │   ├── ch4_training_word_images_gt
│   │   ├── ch4_test_word_images_gt
│   ├── III5K
│   │   ├── train_label.txt
│   │   ├── test_label.txt
│   │   ├── train
│   │   ├── test
│   ├── ct80
│   │   ├── test_label.txt
│   │   ├── image
│   ├── svt
│   │   ├── test_label.txt
│   │   ├── image
│   ├── svtp
│   │   ├── test_label.txt
│   │   ├── image
│   ├── Syn90k
│   │   ├── shuffle_labels.txt
│   │   ├── label.txt
│   │   ├── label.lmdb
│   │   ├── mnt
│   ├── SynthText
│   │   ├── shuffle_labels.txt
│   │   ├── instances_train.txt
│   │   ├── label.txt
│   │   ├── label.lmdb
│   │   ├── synthtext
│   ├── SynthAdd
│   │   ├── label.txt
│   │   ├── label.lmdb
│   │   ├── SynthText_Add
│   ├── TextOCR
│   │   ├── image
│   │   ├── train_label.txt
│   │   ├── val_label.txt
│   ├── Totaltext
│   │   ├── imgs
│   │   ├── annotations
│   │   ├── train_label.txt
│   │   ├── test_label.txt
Dataset images annotation file annotation file
training test
coco_text homepage train_label.txt -
icdar_2011 homepage train_label.txt -
icdar_2013 homepage train_label.txt test_label_1015.txt
icdar_2015 homepage train_label.txt test_label.txt
IIIT5K homepage train_label.txt test_label.txt
ct80 - - test_label.txt
svt homepage - test_label.txt
svtp - - test_label.txt
Syn90k homepage shuffle_labels.txt | label.txt -
SynthText homepage shuffle_labels.txt | instances_train.txt | label.txt -
SynthAdd SynthText_Add.zip (code:627x) label.txt -
TextOCR homepage - -
Totaltext homepage - -

  • For icdar_2013:

  • For icdar_2015:

  • For IIIT5K:

  • For svt:

    python tools/data/textrecog/svt_converter.py <download_svt_dir_path>

  • For ct80:

  • For svtp:

  • For coco_text:

  • For Syn90k:

    mkdir Syn90k && cd Syn90k

mv /path/to/mjsynth.tar.gz .

tar -xzf mjsynth.tar.gz

mv /path/to/shuffle_labels.txt .

# create soft link
cd /path/to/mmocr/data/mixture

ln -s /path/to/Syn90k Syn90k

  • For SynthText:

    • Step1: Download SynthText.zip from homepage
    • Step2:
    mkdir SynthText && cd SynthText
mv /path/to/SynthText.zip .
unzip SynthText.zip
mv SynthText synthtext

mv /path/to/shuffle_labels.txt .

# create soft link
cd /path/to/mmocr/data/mixture
ln -s /path/to/SynthText SynthText
    • Step3: Generate cropped images and labels:
    cd /path/to/mmocr

python tools/data/textrecog/synthtext_converter.py data/mixture/SynthText/gt.mat data/mixture/SynthText/ data/mixture/SynthText/synthtext/SynthText_patch_horizontal --n_proc 8

  • For SynthAdd:

    • Step1: Download SynthText_Add.zip from SynthAdd (code:627x))
    • Step2: Download label.txt
    • Step3:
    mkdir SynthAdd && cd SynthAdd

mv /path/to/SynthText_Add.zip .

unzip SynthText_Add.zip

mv /path/to/label.txt .

# create soft link
cd /path/to/mmocr/data/mixture

ln -s /path/to/SynthAdd SynthAdd

    Note: To convert label file with txt format to lmdb format,

python tools/data/utils/txt2lmdb.py -i <txt_label_path> -o <lmdb_label_path>

For example,

python tools/data/utils/txt2lmdb.py -i data/mixture/Syn90k/label.txt -o data/mixture/Syn90k/label.lmdb

  • For TextOCR:

    mkdir textocr && cd textocr

# Download TextOCR dataset
wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip
wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_train.json
wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_val.json

# For images
unzip -q train_val_images.zip
mv train_images train
    • Step2: Generate train_label.txt, val_label.txt and crop images using 4 processes with the following command:
    python tools/data/textrecog/textocr_converter.py /path/to/textocr 4

  • For Totaltext:

    • Step1: Download totaltext.zip from github dataset and groundtruth_text.zip from github Groundtruth (Our totaltext_converter.py supports groundtruth with both .mat and .txt format).
    mkdir totaltext && cd totaltext
mkdir imgs && mkdir annotations

# For images
# in ./totaltext
unzip totaltext.zip
mv Images/Train imgs/training
mv Images/Test imgs/test

# For annotations
unzip groundtruth_text.zip
cd Groundtruth
mv Polygon/Train ../annotations/training
mv Polygon/Test ../annotations/test
    • Step2: Generate cropped images, train_label.txt and test_label.txt with the following command (the cropped images will be saved to data/totaltext/dst_imgs/.):
    python tools/data/textrecog/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test

Key Information Extraction

The structure of the key information extraction dataset directory is organized as follows.

└── wildreceipt
  ├── class_list.txt
  ├── dict.txt
  ├── image_files
  ├── test.txt
  └── train.txt

Named Entity Recognition

The structure of the named entity recognition dataset directory is organized as follows.

└── cluener2020
  ├── cluener_predict.json
  ├── dev.json
  ├── README.md
  ├── test.json
  ├── train.json
  └── vocab.txt