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# Datasets Preparation
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This page lists the datasets which are commonly used in text detection, text recognition and key information extraction, and their download links.
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<!-- TOC -->
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- [Datasets Preparation ](#datasets-preparation )
- [Text Detection ](#text-detection )
- [Text Recognition ](#text-recognition )
- [Key Information Extraction ](#key-information-extraction )
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- [Named Entity Recognition ](#named-entity-recognition )
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<!-- /TOC -->
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## Text Detection
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The structure of the text detection dataset directory is organized as follows.
```text
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├── ctw1500
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│ ├── annotations
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│ ├── 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
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├── textocr
│ ├── train
│ ├── instances_training.json
│ └── instances_val.json
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├── totaltext
│ ├── imgs
│ ├── instances_test.json
│ └── instances_training.json
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```
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|Dataset|Images| | Annotation Files | | |
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| :-------: | :------------------------------------------------------------: | :----------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | :-------------------------------------: | :--------------------------------------------------------------------------------------------: |
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| | | training | validation | testing | |
| CTW1500 | [homepage ](https://github.com/Yuliang-Liu/Curve-Text-Detector ) | - | - | - |
| ICDAR2015 | [homepage ](https://rrc.cvc.uab.es/?ch=4&com=downloads ) | [instances_training.json ](https://download.openmmlab.com/mmocr/data/icdar2015/instances_training.json ) | - | [instances_test.json ](https://download.openmmlab.com/mmocr/data/icdar2015/instances_test.json ) |
| ICDAR2017 | [homepage ](https://rrc.cvc.uab.es/?ch=8&com=downloads ) | [instances_training.json ](https://download.openmmlab.com/mmocr/data/icdar2017/instances_training.json ) | [instances_val.json ](https://download.openmmlab.com/mmocr/data/icdar2017/instances_val.json ) | - | | |
| Synthtext | [homepage ](https://www.robots.ox.ac.uk/~vgg/data/scenetext/ ) | [instances_training.lmdb ](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb ) | - | - |
| TextOCR | [homepage ](https://textvqa.org/textocr/dataset ) | - | - | -
| Totaltext | [homepage ](https://github.com/cs-chan/Total-Text-Dataset ) | - | - | -
**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 ](https://github.com/open-mmlab/mmocr/blob/main/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py ) for example)
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- For `icdar2015` :
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- Step1: Download `ch4_training_images.zip` , `ch4_test_images.zip` , `ch4_training_localization_transcription_gt.zip` , `Challenge4_Test_Task1_GT.zip` from [homepage ](https://rrc.cvc.uab.es/?ch=4&com=downloads )
- Step2:
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```bash
mkdir icdar2015 & & cd icdar2015
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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
```
- Step3: Download [instances_training.json ](https://download.openmmlab.com/mmocr/data/icdar2015/instances_training.json ) and [instances_test.json ](https://download.openmmlab.com/mmocr/data/icdar2015/instances_test.json ) and move them to `icdar2015`
- Or, generate `instances_training.json` and `instances_test.json` with following command:
```bash
python tools/data/textdet/icdar_converter.py /path/to/icdar2015 -o /path/to/icdar2015 -d icdar2015 --split-list training test
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```
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- For `icdar2017` :
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- Follow similar steps as above.
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- For `ctw1500` :
- Step1: Download `train_images.zip` , `test_images.zip` , `train_labels.zip` , `test_labels.zip` from [github ](https://github.com/Yuliang-Liu/Curve-Text-Detector )
```bash
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
```
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- Step2: Generate `instances_training.json` and `instances_test.json` with following command:
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```bash
python tools/data/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1500 --split-list training test
```
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- For `TextOCR` :
- Step1: Download [train_val_images.zip ](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip ), [TextOCR_0.1_train.json ](https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_train.json ) and [TextOCR_0.1_val.json ](https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_val.json ) to `textocr/` .
```bash
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:
```bash
python tools/data/textdet/textocr_converter.py /path/to/textocr
```
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- For `Totaltext` :
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- Step1: Download `totaltext.zip` from [github dataset ](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset ) and `groundtruth_text.zip` from [github Groundtruth ](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Groundtruth/Text ) (Our totaltext_converter.py supports groundtruth with both .mat and .txt format).
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```bash
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
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```
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- Step2: Generate `instances_training.json` and `instances_test.json` with the following command:
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```bash
python tools/data/textdet/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
```
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## Text Recognition
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**The structure of the text recognition dataset directory is organized as follows.**
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```text
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├── 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
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│ ├── Syn90k
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│ │ ├── shuffle_labels.txt
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│ │ ├── label.txt
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│ │ ├── label.lmdb
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│ │ ├── mnt
│ ├── SynthText
│ │ ├── shuffle_labels.txt
│ │ ├── instances_train.txt
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│ │ ├── label.txt
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│ │ ├── label.lmdb
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│ │ ├── synthtext
│ ├── SynthAdd
│ │ ├── label.txt
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│ │ ├── label.lmdb
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│ │ ├── SynthText_Add
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│ ├── TextOCR
│ │ ├── image
│ │ ├── train_label.txt
│ │ ├── val_label.txt
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│ ├── Totaltext
│ │ ├── imgs
│ │ ├── annotations
│ │ ├── train_label.txt
│ │ ├── test_label.txt
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```
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| Dataset | images | annotation file | annotation file |
| :--------: | :-----------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: |
| | | training | test |
| coco_text | [homepage ](https://rrc.cvc.uab.es/?ch=5&com=downloads ) | [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/coco_text/train_label.txt ) | - | |
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| icdar_2011 | [homepage ](http://www.cvc.uab.es/icdar2011competition/?com=downloads ) | [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/train_label.txt ) | - | |
| icdar_2013 | [homepage ](https://rrc.cvc.uab.es/?ch=2&com=downloads ) | [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/train_label.txt ) | [test_label_1015.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/test_label_1015.txt ) | |
| icdar_2015 | [homepage ](https://rrc.cvc.uab.es/?ch=4&com=downloads ) | [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/train_label.txt ) | [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/test_label.txt ) | |
| IIIT5K | [homepage ](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html ) | [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/train_label.txt ) | [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/test_label.txt ) | |
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| ct80 | [homepage ](http://cs-chan.com/downloads_CUTE80_dataset.html ) | - | [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/ct80/test_label.txt ) | |
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| svt |[homepage ](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset ) | - | [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/svt/test_label.txt ) | |
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| svtp | [unofficial homepage* ](https://github.com/Jyouhou/Case-Sensitive-Scene-Text-Recognition-Datasets ) | - | [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/svtp/test_label.txt ) | |
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| Syn90k | [homepage ](https://www.robots.ox.ac.uk/~vgg/data/text/ ) | [shuffle_labels.txt ](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/shuffle_labels.txt ) \| [label.txt ](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/label.txt ) | - | |
| SynthText | [homepage ](https://www.robots.ox.ac.uk/~vgg/data/scenetext/ ) | [shuffle_labels.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthText/shuffle_labels.txt ) \| [instances_train.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthText/instances_train.txt ) \| [label.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthText/label.txt ) | - | |
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| SynthAdd | [SynthText_Add.zip ](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg ) (code:627x) | [label.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthAdd/label.txt ) | - | |
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| TextOCR | [homepage ](https://textvqa.org/textocr/dataset ) | - | - | |
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| Totaltext | [homepage ](https://github.com/cs-chan/Total-Text-Dataset ) | - | - | |
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(*) Since the official homepage is unavailable now, we provide an alternative for quick reference. However, we do not guarantee the correctness of the dataset.
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- For `icdar_2013` :
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- Step1: Download `Challenge2_Test_Task3_Images.zip` and `Challenge2_Training_Task3_Images_GT.zip` from [homepage ](https://rrc.cvc.uab.es/?ch=2&com=downloads )
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- Step2: Download [test_label_1015.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/test_label_1015.txt ) and [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/train_label.txt )
- For `icdar_2015` :
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- Step1: Download `ch4_training_word_images_gt.zip` and `ch4_test_word_images_gt.zip` from [homepage ](https://rrc.cvc.uab.es/?ch=4&com=downloads )
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- Step2: Download [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/train_label.txt ) and [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/test_label.txt )
- For `IIIT5K` :
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- Step1: Download `IIIT5K-Word_V3.0.tar.gz` from [homepage ](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html )
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- Step2: Download [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/train_label.txt ) and [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/test_label.txt )
- For `svt` :
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- Step1: Download `svt.zip` form [homepage ](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset )
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- Step2: Download [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/svt/test_label.txt )
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- Step3:
```bash
python tools/data/textrecog/svt_converter.py < download_svt_dir_path >
```
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- For `ct80` :
- Step1: Download [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/ct80/test_label.txt )
- For `svtp` :
- Step1: Download [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/svtp/test_label.txt )
- For `coco_text` :
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- Step1: Download from [homepage ](https://rrc.cvc.uab.es/?ch=5&com=downloads )
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- Step2: Download [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/coco_text/train_label.txt )
- For `Syn90k` :
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- Step1: Download `mjsynth.tar.gz` from [homepage ](https://www.robots.ox.ac.uk/~vgg/data/text/ )
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- Step2: Download [shuffle_labels.txt ](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/shuffle_labels.txt )
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- Step3:
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```bash
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
```
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- For `SynthText` :
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- Step1: Download `SynthText.zip` from [homepage ](https://www.robots.ox.ac.uk/~vgg/data/scenetext/ )
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- Step2:
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```bash
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mkdir SynthText & & cd SynthText
mv /path/to/SynthText.zip .
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unzip SynthText.zip
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mv SynthText synthtext
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mv /path/to/shuffle_labels.txt .
# create soft link
cd /path/to/mmocr/data/mixture
ln -s /path/to/SynthText SynthText
```
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- Step3:
Generate cropped images and labels:
```bash
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
```
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- For `SynthAdd` :
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- Step1: Download `SynthText_Add.zip` from [SynthAdd ](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg ) (code:627x))
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- Step2: Download [label.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthAdd/label.txt )
- Step3:
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```bash
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
```
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**Note:**
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To convert label file with `txt` format to `lmdb` format,
```bash
python tools/data/utils/txt2lmdb.py -i < txt_label_path > -o < lmdb_label_path >
```
For example,
```bash
python tools/data/utils/txt2lmdb.py -i data/mixture/Syn90k/label.txt -o data/mixture/Syn90k/label.lmdb
```
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- For `TextOCR` :
- Step1: Download [train_val_images.zip ](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip ), [TextOCR_0.1_train.json ](https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_train.json ) and [TextOCR_0.1_val.json ](https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_val.json ) to `textocr/` .
```bash
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:
```bash
python tools/data/textrecog/textocr_converter.py /path/to/textocr 4
```
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- For `Totaltext` :
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- Step1: Download `totaltext.zip` from [github dataset ](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset ) and `groundtruth_text.zip` from [github Groundtruth ](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Groundtruth/Text ) (Our totaltext_converter.py supports groundtruth with both .mat and .txt format).
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```bash
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/` .):
```bash
python tools/data/textrecog/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
```
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## Key Information Extraction
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The structure of the key information extraction dataset directory is organized as follows.
```text
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└── wildreceipt
├── class_list.txt
├── dict.txt
├── image_files
├── test.txt
└── train.txt
```
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- Download [wildreceipt.tar ](https://download.openmmlab.com/mmocr/data/wildreceipt.tar )
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## Named Entity Recognition
The structure of the named entity recognition dataset directory is organized as follows.
```text
└── cluener2020
├── cluener_predict.json
├── dev.json
├── README.md
├── test.json
├── train.json
└── vocab.txt
```
- Download [cluener_public.zip ](https://storage.googleapis.com/cluebenchmark/tasks/cluener_public.zip )
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- Download [vocab.txt ](https://download.openmmlab.com/mmocr/data/cluener_public/vocab.txt ) and move `vocab.txt` to `cluener2020`
