2021-08-25 16:41:07 +08:00
# Text Detection
## Overview
The structure of the text detection dataset directory is organized as follows.
```text
├── 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
│ ├── data.mdb
│ └── lock.mdb
├── textocr
│ ├── train
│ ├── instances_training.json
│ └── instances_val.json
├── totaltext
│ ├── imgs
│ ├── instances_test.json
│ └── instances_training.json
2022-03-02 11:02:14 +08:00
├── CurvedSynText150k
│ ├── syntext_word_eng
│ ├── emcs_imgs
│ └── instances_training.json
2022-03-04 14:55:54 +10:30
|── funsd
|
│ ├── imgs
│ ├── instances_test.json
│ └── instances_training.json
2022-03-29 11:50:27 +08:00
|── lv
│ ├── imgs
│ ├── instances_test.json
│ └── instances_training.json
│ └── instances_val.json
2021-08-25 16:41:07 +08:00
```
2022-03-04 14:55:54 +10:30
| Dataset | Images | | Annotation Files | | |
| :---------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :---: |
| | | training | validation | testing | |
| CTW1500 | [homepage ](https://github.com/Yuliang-Liu/Curve-Text-Detector ) | - | - | - |
2022-03-30 22:07:17 +08:00
| ICDAR2011 | [homepage ](https://rrc.cvc.uab.es/?ch=1 ) | - | - | |
| ICDAR2013 | [homepage ](https://rrc.cvc.uab.es/?ch=2 ) | - | - | - |
2022-03-04 14:55:54 +10:30
| 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 ([data.mdb ](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/data.mdb ), [lock.mdb ](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/lock.mdb )) | - | - |
| TextOCR | [homepage ](https://textvqa.org/textocr/dataset ) | - | - | - |
| Totaltext | [homepage ](https://github.com/cs-chan/Total-Text-Dataset ) | - | - | - |
| CurvedSynText150k | [homepage ](https://github.com/aim-uofa/AdelaiDet/blob/master/datasets/README.md ) \| [Part1 ](https://drive.google.com/file/d/1OSJ-zId2h3t_-I7g_wUkrK-VqQy153Kj/view?usp=sharing ) \| [Part2 ](https://drive.google.com/file/d/1EzkcOlIgEp5wmEubvHb7-J5EImHExYgY/view?usp=sharing ) | [instances_training.json ](https://download.openmmlab.com/mmocr/data/curvedsyntext/instances_training.json ) | - | - |
| FUNSD | [homepage ](https://guillaumejaume.github.io/FUNSD/ ) | - | - | - |
2022-03-30 14:43:33 +08:00
| DeText | [homepage ](https://rrc.cvc.uab.es/?ch=9 ) | - | - | - |
2022-03-30 14:31:47 +08:00
| NAF | [homepage ](https://github.com/herobd/NAF_dataset/releases/tag/v1.0 ) | - | - | - |
2022-03-30 13:14:23 +08:00
| SROIE | [homepage ](https://rrc.cvc.uab.es/?ch=13 ) | - | - | - |
2022-03-29 11:50:27 +08:00
| Lecture Video DB | [homepage ](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb ) | - | - | - |
2022-03-30 22:07:17 +08:00
| IMGUR | [homepage ](https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset ) | - | - | - |
| KAIST | [homepage ](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database ) | - | - | - |
| MTWI | [homepage ](https://tianchi.aliyun.com/competition/entrance/231685/information?lang=en-us ) | - | - | - |
| COCO Text v2 | [homepage ](https://bgshih.github.io/cocotext/ ) | - | - | - |
| ReCTS | [homepage ](https://rrc.cvc.uab.es/?ch=12 ) | - | - | - |
2022-03-04 14:55:54 +10:30
2021-08-25 16:41:07 +08:00
## Important Note
2021-09-08 11:40:51 +08:00
:::{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
2021-08-25 16:41:07 +08:00
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
2022-01-05 21:44:13 +08:00
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 pipeline config ](https://github.com/open-mmlab/mmocr/blob/main/configs/_base_/det_pipelines/dbnet_pipeline.py ) for example)
2021-09-08 11:40:51 +08:00
:::
2021-08-25 16:41:07 +08:00
## Preparation Steps
### ICDAR 2015
- Step0: Read [Important Note ](#important-note )
- 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:
```bash
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
```
- 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
```
### ICDAR 2017
- Follow similar steps as [ICDAR 2015 ](#icdar-2015 ).
### CTW1500
- Step0: Read [Important Note ](#important-note )
- 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
```
- Step2: Generate `instances_training.json` and `instances_test.json` with following command:
```bash
python tools/data/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1500 --split-list training test
```
2022-03-30 22:07:17 +08:00
### ICDAR 2011 (Born-Digital Images)
- Step1: Download `Challenge1_Training_Task12_Images.zip` , `Challenge1_Training_Task1_GT.zip` , `Challenge1_Test_Task12_Images.zip` , and `Challenge1_Test_Task1_GT.zip` from [homepage ](https://rrc.cvc.uab.es/?ch=1&com=downloads ) `Task 1.1: Text Localization (2013 edition)` .
```bash
mkdir icdar2011 & & cd icdar2011
mkdir imgs & & mkdir annotations
# Download ICDAR 2011
wget https://rrc.cvc.uab.es/downloads/Challenge1_Training_Task12_Images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/Challenge1_Training_Task1_GT.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/Challenge1_Test_Task12_Images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/Challenge1_Test_Task1_GT.zip --no-check-certificate
# For images
unzip -q Challenge1_Training_Task12_Images.zip -d imgs/training
unzip -q Challenge1_Test_Task12_Images.zip -d imgs/test
# For annotations
unzip -q Challenge1_Training_Task1_GT.zip -d annotations/training
unzip -q Challenge1_Test_Task1_GT.zip -d annotations/test
rm Challenge1_Training_Task12_Images.zip & & rm Challenge1_Test_Task12_Images.zip & & rm Challenge1_Training_Task1_GT.zip & & rm Challenge1_Test_Task1_GT.zip
```
- Step 2: Generate `instances_training.json` and `instances_test.json` with the following command:
```bash
python tools/data/textdet/ic11_converter.py PATH/TO/icdar2011 --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
|── icdar2011
│ ├── imgs
│ ├── instances_test.json
│ └── instances_training.json
```
### ICDAR 2013 (Focused Scene Text)
- Step1: Download `Challenge2_Training_Task12_Images.zip` , `Challenge2_Test_Task12_Images.zip` , `Challenge2_Training_Task1_GT.zip` , and `Challenge2_Test_Task1_GT.zip` from [homepage ](https://rrc.cvc.uab.es/?ch=2&com=downloads ) `Task 2.1: Text Localization (2013 edition)` .
```bash
mkdir icdar2013 & & cd icdar2013
mkdir imgs & & mkdir annotations
# Download ICDAR 2013
wget https://rrc.cvc.uab.es/downloads/Challenge2_Training_Task12_Images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/Challenge2_Test_Task12_Images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/Challenge2_Training_Task1_GT.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/Challenge2_Test_Task1_GT.zip --no-check-certificate
# For images
unzip -q Challenge2_Training_Task12_Images.zip -d imgs/training
unzip -q Challenge2_Test_Task12_Images.zip -d imgs/test
# For annotations
unzip -q Challenge2_Training_Task1_GT.zip -d annotations/training
unzip -q Challenge2_Test_Task1_GT.zip -d annotations/test
rm Challenge2_Training_Task12_Images.zip & & rm Challenge2_Test_Task12_Images.zip & & rm Challenge2_Training_Task1_GT.zip & & rm Challenge2_Test_Task1_GT.zip
```
- Step 2: Generate `instances_training.json` and `instances_test.json` with the following command:
```bash
python tools/data/textdet/ic13_converter.py PATH/TO/icdar2013 --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
|── icdar2013
│ ├── imgs
│ ├── instances_test.json
│ └── instances_training.json
```
2021-08-25 16:41:07 +08:00
### SynthText
- Download [data.mdb ](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/data.mdb ) and [lock.mdb ](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/lock.mdb ) to `synthtext/instances_training.lmdb/` .
### 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
```
### Totaltext
- Step0: Read [Important Note ](#important-note )
- 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).
```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 `instances_training.json` and `instances_test.json` with the following command:
```bash
python tools/data/textdet/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
```
2022-03-02 11:02:14 +08:00
### CurvedSynText150k
- Step1: Download [syntext1.zip ](https://drive.google.com/file/d/1OSJ-zId2h3t_-I7g_wUkrK-VqQy153Kj/view?usp=sharing ) and [syntext2.zip ](https://drive.google.com/file/d/1EzkcOlIgEp5wmEubvHb7-J5EImHExYgY/view?usp=sharing ) to `CurvedSynText150k/` .
- Step2:
```bash
unzip -q syntext1.zip
mv train.json train1.json
unzip images.zip
rm images.zip
unzip -q syntext2.zip
mv train.json train2.json
unzip images.zip
rm images.zip
```
- Step3: Download [instances_training.json ](https://download.openmmlab.com/mmocr/data/curvedsyntext/instances_training.json ) to `CurvedSynText150k/`
- Or, generate `instances_training.json` with following command:
```bash
python tools/data/common/curvedsyntext_converter.py PATH/TO/CurvedSynText150k --nproc 4
```
2022-03-04 14:55:54 +10:30
### FUNSD
- Step1: Download [dataset.zip ](https://guillaumejaume.github.io/FUNSD/dataset.zip ) to `funsd/` .
```bash
mkdir funsd & & cd funsd
# Download FUNSD dataset
wget https://guillaumejaume.github.io/FUNSD/dataset.zip
unzip -q dataset.zip
# For images
mv dataset/training_data/images imgs & & mv dataset/testing_data/images/* imgs/
# For annotations
mkdir annotations
mv dataset/training_data/annotations annotations/training & & mv dataset/testing_data/annotations annotations/test
rm dataset.zip & & rm -rf dataset
```
- Step2: Generate `instances_training.json` and `instances_test.json` with following command:
```bash
python tools/data/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
```
2022-03-30 14:43:33 +08:00
### DeText
- Step1: Download `ch9_training_images.zip` , `ch9_training_localization_transcription_gt.zip` , `ch9_validation_images.zip` , and `ch9_validation_localization_transcription_gt.zip` from **Task 3: End to End** on the [homepage ](https://rrc.cvc.uab.es/?ch=9 ).
```bash
mkdir detext & & cd detext
mkdir imgs & & mkdir annotations & & mkdir imgs/training & & mkdir imgs/val & & mkdir annotations/training & & mkdir annotations/val
# Download DeText
wget https://rrc.cvc.uab.es/downloads/ch9_training_images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/ch9_training_localization_transcription_gt.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/ch9_validation_images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/ch9_validation_localization_transcription_gt.zip --no-check-certificate
# Extract images and annotations
unzip -q ch9_training_images.zip -d imgs/training & & unzip -q ch9_training_localization_transcription_gt.zip -d annotations/training & & unzip -q ch9_validation_images.zip -d imgs/val & & unzip -q ch9_validation_localization_transcription_gt.zip -d annotations/val
# Remove zips
rm ch9_training_images.zip & & rm ch9_training_localization_transcription_gt.zip & & rm ch9_validation_images.zip & & rm ch9_validation_localization_transcription_gt.zip
```
- Step2: Generate `instances_training.json` and `instances_val.json` with following command:
```bash
python tools/data/textdet/detext_converter.py PATH/TO/detext --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
|── detext
|
│ ├── imgs
│ ├── instances_test.json
│ └── instances_training.json
```
2022-03-29 11:50:27 +08:00
2022-03-30 14:31:47 +08:00
### NAF
- Step1: Download [labeled_images.tar.gz ](https://github.com/herobd/NAF_dataset/releases/tag/v1.0 ) to `naf/` .
```bash
mkdir naf & & cd naf
# Download NAF dataset
wget https://github.com/herobd/NAF_dataset/releases/download/v1.0/labeled_images.tar.gz
tar -zxf labeled_images.tar.gz
# For images
mkdir annotations & & mv labeled_images imgs
# For annotations
git clone https://github.com/herobd/NAF_dataset.git
mv NAF_dataset/train_valid_test_split.json annotations/ & & mv NAF_dataset/groups annotations/
rm -rf NAF_dataset & & rm labeled_images.tar.gz
```
- Step2: Generate `instances_training.json` , `instances_val.json` , and `instances_test.json` with following command:
```bash
python tools/data/textdet/naf_converter.py PATH/TO/naf --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
|── naf
|
│ ├── imgs
│ ├── instances_test.json
│ ├── instances_val.json
│ └── instances_training.json
```
2022-03-30 13:14:23 +08:00
### SROIE
- Step1: Download `0325updated.task1train(626p).zip` , `task1&2_test(361p).zip` , and `text.task1&2-test（ from [homepage ](https://rrc.cvc.uab.es/?ch=13&com=downloads ) to `sroie/`
- Step2:
```bash
mkdir sroie & & cd sroie
mkdir imgs & & mkdir annotations & & mkdir imgs/training
# Warnninig: The zip files downloaded from Google Drive and BaiduYun Cloud may
# be different, the user should revise the following commands to the correct
# file name if encounter with errors while extracting and move the files.
unzip -q 0325updated.task1train\(626p\).zip && unzip -q task1\&2_test\(361p\).zip && unzip -q text.task1\&2-test（
# For images
mv 0325updated.task1train\(626p\)/*.jpg imgs/training && mv fulltext_test\(361p\) imgs/test
# For annotations
mv 0325updated.task1train\(626p\) annotations/training && mv text.task1\&2-testг
rm 0325updated.task1train\(626p\).zip && rm task1\&2_test\(361p\).zip && rm text.task1\&2-test（
```
- Step3: Generate `instances_training.json` and `instances_test.json` with the following command:
```bash
python tools/data/textdet/sroie_converter.py PATH/TO/sroie --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
├── sroie
│ ├── annotations
│ ├── imgs
│ ├── instances_test.json
│ └── instances_training.json
```
2022-03-29 11:50:27 +08:00
### Lecture Video DB
- Step1: Download [IIIT-CVid.zip ](http://cdn.iiit.ac.in/cdn/preon.iiit.ac.in/~kartik/IIIT-CVid.zip ) to `lv/` .
```bash
mkdir lv & & cd lv
# Download LV dataset
wget http://cdn.iiit.ac.in/cdn/preon.iiit.ac.in/~kartik/IIIT-CVid.zip
unzip -q IIIT-CVid.zip
mv IIIT-CVid/Frames imgs
rm IIIT-CVid.zip
```
- Step2: Generate `instances_training.json` , `instances_val.json` , and `instances_test.json` with following command:
```bash
python tools/data/textdet/lv_converter.py PATH/TO/lv --nproc 4
```
2022-03-30 22:07:17 +08:00
### IMGUR
- Step1: Run `download_imgur5k.py` to download images. You can merge [PR#5 ](https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset/pull/5 ) in your local repository to enable a **much faster** parallel execution of image download.
```bash
mkdir imgur & & cd imgur
git clone https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset.git
# Download images from imgur.com. This may take SEVERAL HOURS!
python ./IMGUR5K-Handwriting-Dataset/download_imgur5k.py --dataset_info_dir ./IMGUR5K-Handwriting-Dataset/dataset_info/ --output_dir ./imgs
# For annotations
mkdir annotations
mv ./IMGUR5K-Handwriting-Dataset/dataset_info/*.json annotations
rm -rf IMGUR5K-Handwriting-Dataset
```
- Step2: Generate `instances_train.json` , `instance_val.json` and `instances_test.json` with the following command:
```bash
python tools/data/textdet/imgur_converter.py PATH/TO/imgur
```
- After running the above codes, the directory structure should be as follows:
```
|── imgur
| ├── annotations
│ ├── imgs
│ ├── instances_test.json
│ ├── instances_training.json
│ └── instances_val.json
```
### KAIST
- Step1: Complete download [KAIST_all.zip ](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database ) to `kaist/` .
```bash
mkdir kaist & & cd kaist
mkdir imgs & & mkdir annotations
# Download KAIST dataset
wget http://www.iapr-tc11.org/dataset/KAIST_SceneText/KAIST_all.zip
unzip -q KAIST_all.zip
rm KAIST_all.zip
```
- Step2: Extract zips:
```bash
python tools/data/common/extract_kaist.py PATH/TO/kaist
```
- Step3: Generate `instances_training.json` and `instances_val.json` (optional) with following command:
```bash
# Since KAIST does not provide an official split, you can split the dataset by adding --val-ratio 0.2
python tools/data/textdet/kaist_converter.py PATH/TO/kaist --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
|── kaist
| ├── annotations
│ ├── imgs
│ ├── instances_training.json
│ └── instances_val.json (optional)
```
### MTWI
- Step1: Download `mtwi_2018_train.zip` from [homepage ](https://tianchi.aliyun.com/competition/entrance/231685/information?lang=en-us ).
```bash
mkdir mtwi & & cd mtwi
unzip -q mtwi_2018_train.zip
mv image_train imgs & & mv txt_train annotations
rm mtwi_2018_train.zip
```
- Step2: Generate `instances_training.json` and `instance_val.json` (optional) with the following command:
```bash
# Annotations of MTWI test split is not publicly available, split a validation
# set by adding --val-ratio 0.2
python tools/data/textdet/mtwi_converter.py PATH/TO/mtwi --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
|── mtwi
| ├── annotations
│ ├── imgs
│ ├── instances_training.json
│ └── instances_val.json (optional)
```
### COCO Text v2
- Step1: Download image [train2014.zip ](http://images.cocodataset.org/zips/train2014.zip ) and annotation [cocotext.v2.zip ](https://github.com/bgshih/cocotext/releases/download/dl/cocotext.v2.zip ) to `coco_textv2/` .
```bash
mkdir coco_textv2 & & cd coco_textv2
mkdir annotations
# Download COCO Text v2 dataset
wget http://images.cocodataset.org/zips/train2014.zip
wget https://github.com/bgshih/cocotext/releases/download/dl/cocotext.v2.zip
unzip -q train2014.zip & & unzip -q cocotext.v2.zip
mv train2014 imgs & & mv cocotext.v2.json annotations/
rm train2014.zip & & rm -rf cocotext.v2.zip
```
- Step2: Generate `instances_training.json` and `instances_val.json` with the following command:
```bash
python tools/data/textdet/cocotext_converter.py PATH/TO/coco_textv2
```
- After running the above codes, the directory structure should be as follows:
```text
|── coco_textv2
| ├── annotations
│ ├── imgs
│ ├── instances_training.json
│ └── instances_val.json
```
### ReCTS
- Step1: Download [ReCTS.zip ](https://datasets.cvc.uab.es/rrc/ReCTS.zip ) to `rects/` from the [homepage ](https://rrc.cvc.uab.es/?ch=12&com=downloads ).
```bash
mkdir rects & & cd rects
# Download ReCTS dataset
# You can also find Google Drive link on the dataset homepage
wget https://datasets.cvc.uab.es/rrc/ReCTS.zip --no-check-certificate
unzip -q ReCTS.zip
mv img imgs & & mv gt_unicode annotations
rm ReCTS.zip & & rm -rf gt
```
- Step2: Generate `instances_training.json` and `instances_val.json` (optional) with following command:
```bash
# Annotations of ReCTS test split is not publicly available, split a validation
# set by adding --val-ratio 0.2
# Add --preserve-vertical to preserve vertical texts for training, otherwise
# vertical images will be filtered and stored in PATH/TO/rects/ignores
python tools/data/textdet/rects_converter.py PATH/TO/rects --nproc 4 --val-ratio 0.2
```
- After running the above codes, the directory structure should be as follows:
```text
|── rects
| ├── annotations
│ ├── imgs
│ ├── instances_val.json (optional)
│ └── instances_training.json
```
