# Text Recognition

```{note}
This page is deprecated and all these scripts will be eventually migrated into dataset preparer, a brand new module designed to ease these lengthy dataset preparation steps. [Check it out](./dataset_preparer.md)!
```

## Overview

|        Dataset        |                        images                         |                         annotation file                         |                         annotation file                         |
| :-------------------: | :---------------------------------------------------: | :-------------------------------------------------------------: | :-------------------------------------------------------------: |
|                       |                                                       |                            training                             |                              test                               |
|       coco_text       | [homepage](https://rrc.cvc.uab.es/?ch=5&com=downloads) |                   [train_labels.json](#TODO)                    |                                -                                |
|       ICDAR2011       |       [homepage](https://rrc.cvc.uab.es/?ch=1)        |                                -                                |                                -                                |
|       ICDAR2013       |       [homepage](https://rrc.cvc.uab.es/?ch=2)        |                                -                                |                                -                                |
|      icdar_2015       | [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads) | [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/icdar_2015/train_labels.json) | [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/icdar_2015/test_labels.json) |
|        IIIT5K         | [homepage](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html) | [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/IIIT5K/train_labels.json) | [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/IIIT5K/test_labels.json) |
|         ct80          | [homepage](http://cs-chan.com/downloads_CUTE80_dataset.html) |                                -                                | [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/ct80/test_labels.json) |
|          svt          | [homepage](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset) |                                -                                | [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/svt/test_labels.json) |
|         svtp          | [unofficial homepage\[1\]](https://github.com/Jyouhou/Case-Sensitive-Scene-Text-Recognition-Datasets) |                                -                                | [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/svtp/test_labels.json) |
|   MJSynth (Syn90k)    | [homepage](https://www.robots.ox.ac.uk/~vgg/data/text/) | [subset_train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/Syn90k/subset_train_labels.json) \| [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/Syn90k/train_labels.json) |                                -                                |
| SynthText (Synth800k) | [homepage](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) | [alphanumeric_train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/SynthText/alphanumeric_train_labels.json) \|[subset_train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/SynthText/subset_train_labels.json) \|  [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/SynthText/train_labels.json) |                                -                                |
|       SynthAdd        | [SynthText_Add.zip](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg)  (code:627x) | [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/synthtext_add/train_labels.json) |                                -                                |
|        TextOCR        |    [homepage](https://textvqa.org/textocr/dataset)    |                                -                                |                                -                                |
|       Totaltext       | [homepage](https://github.com/cs-chan/Total-Text-Dataset) |                                -                                |                                -                                |
|       OpenVINO        | [Open Images](https://github.com/cvdfoundation/open-images-dataset) | [annotations](https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text) | [annotations](https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text) |
|         FUNSD         |  [homepage](https://guillaumejaume.github.io/FUNSD/)  |                                -                                |                                -                                |
|        DeText         |       [homepage](https://rrc.cvc.uab.es/?ch=9)        |                                -                                |                                -                                |
|          NAF          |   [homepage](https://github.com/herobd/NAF_dataset)   |                                -                                |                                -                                |
|         SROIE         |       [homepage](https://rrc.cvc.uab.es/?ch=13)       |                                -                                |                                -                                |
|   Lecture Video DB    | [homepage](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb) |                                -                                |                                -                                |
|         LSVT          |       [homepage](https://rrc.cvc.uab.es/?ch=16)       |                                -                                |                                -                                |
|         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)       |                                -                                |                                -                                |
|       IIIT-ILST       | [homepage](http://cvit.iiit.ac.in/research/projects/cvit-projects/iiit-ilst) |                                -                                |                                -                                |
|        VinText        | [homepage](https://github.com/VinAIResearch/dict-guided) |                                -                                |                                -                                |
|          BID          | [homepage](https://github.com/ricardobnjunior/Brazilian-Identity-Document-Dataset) |                                -                                |                                -                                |
|         RCTW          |    [homepage](https://rctw.vlrlab.net/index.html)     |                                -                                |                                -                                |
|       HierText        | [homepage](https://github.com/google-research-datasets/hiertext) |                                -                                |                                -                                |
|          ArT          |       [homepage](https://rrc.cvc.uab.es/?ch=14)       |                                -                                |                                -                                |

(\*) Since the official homepage is unavailable now, we provide an alternative for quick reference. However, we do not guarantee the correctness of the dataset.

### Install AWS CLI (optional)

- Since there are some datasets that require the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) to be installed in advance, we provide a quick installation guide here:

  ```bash
    curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
    unzip awscliv2.zip
    sudo ./aws/install
    ./aws/install -i /usr/local/aws-cli -b /usr/local/bin
    !aws configure
    # this command will require you to input keys, you can skip them except
    # for the Default region name
    # AWS Access Key ID [None]:
    # AWS Secret Access Key [None]:
    # Default region name [None]: us-east-1
    # Default output format [None]
  ```

## ICDAR 2011 (Born-Digital Images)

- Step1: Download `Challenge1_Training_Task3_Images_GT.zip`, `Challenge1_Test_Task3_Images.zip`, and `Challenge1_Test_Task3_GT.txt` from [homepage](https://rrc.cvc.uab.es/?ch=1&com=downloads) `Task 1.3: Word Recognition (2013 edition)`.

  ```bash
  mkdir icdar2011 && cd icdar2011
  mkdir annotations

  # Download ICDAR 2011
  wget https://rrc.cvc.uab.es/downloads/Challenge1_Training_Task3_Images_GT.zip --no-check-certificate
  wget https://rrc.cvc.uab.es/downloads/Challenge1_Test_Task3_Images.zip --no-check-certificate
  wget https://rrc.cvc.uab.es/downloads/Challenge1_Test_Task3_GT.txt --no-check-certificate

  # For images
  mkdir crops
  unzip -q Challenge1_Training_Task3_Images_GT.zip -d crops/train
  unzip -q Challenge1_Test_Task3_Images.zip -d crops/test

  # For annotations
  mv Challenge1_Test_Task3_GT.txt annotations && mv crops/train/gt.txt annotations/Challenge1_Train_Task3_GT.txt
  ```

- Step2: Convert original annotations to `train_labels.json` and `test_labels.json` with the following command:

  ```bash
  python tools/dataset_converters/textrecog/ic11_converter.py PATH/TO/icdar2011
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── icdar2011
  │   ├── crops
  │   ├── train_labels.json
  │   └── test_labels.json
  ```

## ICDAR 2013 (Focused Scene Text)

- Step1: Download `Challenge2_Training_Task3_Images_GT.zip`, `Challenge2_Test_Task3_Images.zip`, and `Challenge2_Test_Task3_GT.txt` from [homepage](https://rrc.cvc.uab.es/?ch=2&com=downloads) `Task 2.3: Word Recognition (2013 edition)`.

  ```bash
  mkdir icdar2013 && cd icdar2013
  mkdir annotations

  # Download ICDAR 2013
  wget https://rrc.cvc.uab.es/downloads/Challenge2_Training_Task3_Images_GT.zip --no-check-certificate
  wget https://rrc.cvc.uab.es/downloads/Challenge2_Test_Task3_Images.zip --no-check-certificate
  wget https://rrc.cvc.uab.es/downloads/Challenge2_Test_Task3_GT.txt --no-check-certificate
  wget https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/test_label_1015.txt

  # For images
  mkdir crops
  unzip -q Challenge2_Training_Task3_Images_GT.zip -d crops/train
  unzip -q Challenge2_Test_Task3_Images.zip -d crops/test
  # For annotations
  mv Challenge2_Test_Task3_GT.txt annotations && mv crops/train/gt.txt annotations/Challenge2_Train_Task3_GT.txt && mv test_label_1015.txt annotations/Challenge2_Test1015_Task3_GT.txt

  rm Challenge2_Training_Task3_Images_GT.zip && rm Challenge2_Test_Task3_Images.zip
  ```

- Step 2: Generate `train_labels.json`, `test_labels.json`, `test1015_label.json` with the following command:

  ```bash
  python tools/dataset_converters/textrecog/ic13_converter.py PATH/TO/icdar2013
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── icdar2013
  │   ├── crops
  │   ├── train_labels.json
  │   ├── test_labels.json
  │   └── test1015_label.json
  ```

## ICDAR 2015

- 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)

- Step2: Download [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/icdar_2015/train_labels.json) and [test_label.json](https://download.openmmlab.com/mmocr/data/1.x/recog/icdar_2015/test_labels.json)

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── icdar_2015
  │   ├── train_labels.json
  │   ├── test_labels.json
  │   ├── ch4_training_word_images_gt
  │   └── ch4_test_word_images_gt
  ```

## IIIT5K

- Step1: Download `IIIT5K-Word_V3.0.tar.gz` from [homepage](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html)

- Step2: Download [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/IIIT5K/train_labels.json) and [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/IIIT5K/test_labels.json)

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── III5K
  │   ├── train_labels.json
  │   ├── test_labels.json
  │   ├── train
  │   └── test
  ```

## svt

- Step1: Download `svt.zip` form [homepage](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset)

- Step2: Download [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/svt/test_labels.json)

- Step3:

  ```bash
  python tools/dataset_converters/textrecog/svt_converter.py <download_svt_dir_path>
  ```

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── svt
  │   ├── test_labels.json
  │   └── image
  ```

## ct80

- Step1: Download [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/ct80/test_labels.json)

- Step2: Download [timage.tar.gz](https://download.openmmlab.com/mmocr/data/mixture/ct80/timage.tar.gz)

- Step3:

  ```bash
  mkdir ct80 && cd ct80
  mv /path/to/test_labels.json .
  mv /path/to/timage.tar.gz .
  tar -xvf timage.tar.gz
  # create soft link
  cd /path/to/mmocr/data/mixture
  ln -s /path/to/ct80 ct80
  ```

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── ct80
  │   ├── test_labels.json
  │   └── timage
  ```

## svtp

- Step1: Download [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/svtp/test_labels.json)

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── svtp
  │   ├── test_labels.json
  │   └── image
  ```

## coco_text

- Step1: Download from [homepage](https://rrc.cvc.uab.es/?ch=5&com=downloads)

- Step2: Download [train_labels.json](https://download.openmmlab.com/mmocr/data/mixture/coco_text/train_labels.json)

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── coco_text
  │   ├── train_labels.json
  │   └── train_words
  ```

## MJSynth (Syn90k)

- Step1: Download `mjsynth.tar.gz` from [homepage](https://www.robots.ox.ac.uk/~vgg/data/text/)
- Step2: Download [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/Syn90k/train_labels.json) (8,919,273 annotations) and [subset_train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/Syn90k/subset_train_labels.json) (2,400,000 randomly sampled annotations).

```{note}
Please make sure you're using the right annotation to train the model by checking its dataset specs in Model Zoo.
```

- Step3:

  ```bash
  mkdir Syn90k && cd Syn90k

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

  tar -xzf mjsynth.tar.gz

  mv /path/to/subset_train_labels.json .
  mv /path/to/train_labels.json .

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

  ln -s /path/to/Syn90k Syn90k

  ```

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── Syn90k
  │   ├── subset_train_labels.json
  │   ├── train_labels.json
  │   └── mnt
  ```

## SynthText (Synth800k)

- Step1: Download `SynthText.zip` from [homepage](https://www.robots.ox.ac.uk/~vgg/data/scenetext/)

- Step2: According to your actual needs, download the most appropriate one from the following options: [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/SynthText/train_labels.json) (7,266,686 annotations), [subset_train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/SynthText/subset_train_labels.json) (2,400,000 randomly sampled annotations) and [alphanumeric_train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/SynthText/alphanumeric_train_labels.json) (7,239,272 annotations with alphanumeric characters only).

```{warning}
Please make sure you're using the right annotation to train the model by checking its dataset specs in Model Zoo.
```

- Step3:

  ```bash
  mkdir SynthText && cd SynthText
  mv /path/to/SynthText.zip .
  unzip SynthText.zip
  mv SynthText synthtext

  mv /path/to/subset_train_labels.json .
  mv /path/to/train_labels.json .
  mv /path/to/alphanumeric_train_labels.json .

  # create soft link
  cd /path/to/mmocr/data/recog
  ln -s /path/to/SynthText SynthText
  ```

- Step4: Generate cropped images and labels:

  ```bash
  cd /path/to/mmocr

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

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── SynthText
  │   ├── alphanumeric_train_labels.json
  │   ├── subset_train_labels.json
  │   ├── train_labels.json
  │   └── synthtext
  ```

## SynthAdd

- Step1: Download `SynthText_Add.zip` from [SynthAdd](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg) (code:627x))

- Step2: Download [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/synthtext_add/train_labels.json)

- Step3:

  ```bash
  mkdir SynthAdd && cd SynthAdd

  mv /path/to/SynthText_Add.zip .

  unzip SynthText_Add.zip

  mv /path/to/train_labels.json .

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

  ln -s /path/to/SynthAdd SynthAdd

  ```

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── SynthAdd
  │   ├── train_labels.json
  │   └── SynthText_Add
  ```

## 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_labels.json`, `val_labels.json` and crop images using 4 processes with the following command:

  ```bash
  python tools/dataset_converters/textrecog/textocr_converter.py /path/to/textocr 4
  ```

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── TextOCR
  │   ├── image
  │   ├── train_labels.json
  │   └── val_labels.json
  ```

## Totaltext

- Step1: Download `totaltext.zip` from [github dataset](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset) and `groundtruth_text.zip` or `TT_new_train_GT.zip` (if you prefer to use the latest version of training annotations) 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 legacy training and test annotations
  unzip groundtruth_text.zip
  mv Groundtruth/Polygon/Train annotations/training
  mv Groundtruth/Polygon/Test annotations/test

  # Using the latest training annotations
  # WARNING: Delete legacy train annotations before running the following command.
  unzip TT_new_train_GT.zip
  mv Train annotations/training
  ```

- Step2: Generate cropped images, `train_labels.json` and `test_labels.json` with the following command (the cropped images will be saved to `data/totaltext/dst_imgs/`):

  ```bash
  python tools/dataset_converters/textrecog/totaltext_converter.py /path/to/totaltext
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── totaltext
  │   ├── dst_imgs
  │   ├── train_labels.json
  │   └── test_labels.json
  ```

## OpenVINO

- Step1 (optional): Install [AWS CLI](https://mmocr.readthedocs.io/en/latest/datasets/recog.html#install-aws-cli-optional).

- Step2: Download [Open Images](https://github.com/cvdfoundation/open-images-dataset#download-images-with-bounding-boxes-annotations) subsets `train_1`, `train_2`, `train_5`, `train_f`, and `validation` to `openvino/`.

  ```bash
  mkdir openvino && cd openvino

  # Download Open Images subsets
  for s in 1 2 5 f; do
    aws s3 --no-sign-request cp s3://open-images-dataset/tar/train_${s}.tar.gz .
  done
  aws s3 --no-sign-request cp s3://open-images-dataset/tar/validation.tar.gz .

  # Download annotations
  for s in 1 2 5 f; do
    wget https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text/text_spotting_openimages_v5_train_${s}.json
  done
  wget https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text/text_spotting_openimages_v5_validation.json

  # Extract images
  mkdir -p openimages_v5/val
  for s in 1 2 5 f; do
    tar zxf train_${s}.tar.gz -C openimages_v5
  done
  tar zxf validation.tar.gz -C openimages_v5/val
  ```

- Step3: Generate `train_{1,2,5,f}_labels.json`, `val_labels.json` and crop images using 4 processes with the following command:

  ```bash
  python tools/dataset_converters/textrecog/openvino_converter.py /path/to/openvino 4
  ```

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── OpenVINO
  │   ├── image_1
  │   ├── image_2
  │   ├── image_5
  │   ├── image_f
  │   ├── image_val
  │   ├── train_1_labels.json
  │   ├── train_2_labels.json
  │   ├── train_5_labels.json
  │   ├── train_f_labels.json
  │   └── val_labels.json
  ```

## 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 `train_labels.json` and `test_labels.json` with following command:

  ```bash
  # Add --preserve-vertical to preserve vertical texts for training, otherwise
  # vertical images will be filtered and stored in PATH/TO/detext/ignores
  python tools/dataset_converters/textrecog/detext_converter.py PATH/TO/detext --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── detext
  │   ├── crops
  │   ├── ignores
  │   ├── train_labels.json
  │   └── test_labels.json
  ```

## 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 `train_labels.json`, `val_labels.json`, and `test_labels.json` with following command:

  ```bash
  # Add --preserve-vertical to preserve vertical texts for training, otherwise
  # vertical images will be filtered and stored in PATH/TO/naf/ignores
  python tools/dataset_converters/textrecog/naf_converter.py PATH/TO/naf --nproc 4

  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── naf
  │   ├── crops
  │   ├── train_labels.json
  │   ├── val_labels.json
  │   └── test_labels.json
  ```

## SROIE

- Step1: Step1: Download `0325updated.task1train(626p).zip`, `task1&2_test(361p).zip`, and `text.task1&2-test（361p).zip` 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（361p\).zip

  # 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-testги361p\)/ annotations/test

  rm 0325updated.task1train\(626p\).zip && rm task1\&2_test\(361p\).zip && rm text.task1\&2-test（361p\).zip
  ```

- Step3: Generate `train_labels.json` and `test_labels.json` and crop images using 4 processes with the following command:

  ```bash
  python tools/dataset_converters/textrecog/sroie_converter.py PATH/TO/sroie --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── sroie
  │   ├── crops
  │   ├── train_labels.json
  │   └── test_labels.json
  ```

## Lecture Video DB

```{warning}
This section is not fully tested yet.
```

```{note}
The LV dataset has already provided cropped images and the corresponding annotations
```

- 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

  # For image
  mv IIIT-CVid/Crops ./

  # For annotation
  mv IIIT-CVid/train.txt train_labels.json && mv IIIT-CVid/val.txt val_label.txt && mv IIIT-CVid/test.txt test_labels.json

  rm IIIT-CVid.zip
  ```

- Step2: Generate `train_labels.json`, `val.json`, and `test.json` with following command:

  ```bash
  python tools/dataset_converters/textdreog/lv_converter.py PATH/TO/lv
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── lv
  │   ├── Crops
  │   ├── train_labels.json
  │   └── test_labels.json
  ```

## LSVT

```{warning}
This section is not fully tested yet.
```

- Step1: Download [train_full_images_0.tar.gz](https://dataset-bj.cdn.bcebos.com/lsvt/train_full_images_0.tar.gz), [train_full_images_1.tar.gz](https://dataset-bj.cdn.bcebos.com/lsvt/train_full_images_1.tar.gz), and [train_full_labels.json](https://dataset-bj.cdn.bcebos.com/lsvt/train_full_labels.json) to `lsvt/`.

  ```bash
  mkdir lsvt && cd lsvt

  # Download LSVT dataset
  wget https://dataset-bj.cdn.bcebos.com/lsvt/train_full_images_0.tar.gz
  wget https://dataset-bj.cdn.bcebos.com/lsvt/train_full_images_1.tar.gz
  wget https://dataset-bj.cdn.bcebos.com/lsvt/train_full_labels.json

  mkdir annotations
  tar -xf train_full_images_0.tar.gz && tar -xf train_full_images_1.tar.gz
  mv train_full_labels.json annotations/ && mv train_full_images_1/*.jpg train_full_images_0/
  mv train_full_images_0 imgs

  rm train_full_images_0.tar.gz && rm train_full_images_1.tar.gz && rm -rf train_full_images_1
  ```

- Step2: Generate `train_labels.json` and `val_label.json` (optional) with the following command:

  ```bash
  # Annotations of LSVT 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/lsvt/ignores
  python tools/dataset_converters/textdrecog/lsvt_converter.py PATH/TO/lsvt --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── lsvt
  │   ├── crops
  │   ├── ignores
  │   ├── train_labels.json
  │   └── val_label.json (optional)
  ```

## FUNSD

```{warning}
This section is not fully tested yet.
```

- 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 `train_labels.json` and `test_labels.json` and crop images using 4 processes with following command (add `--preserve-vertical` if you wish to preserve the images containing vertical texts):

  ```bash
  python tools/dataset_converters/textrecog/funsd_converter.py PATH/TO/funsd --nproc 4
  ```

- After running the above codes, the directory structure
  should be as follows:

  ```text
  ├── funsd
  │   ├── imgs
  │   ├── crops
  │   ├── annotations
  │   ├── train_labels.json
  │   └── test_labels.json
  ```

## IMGUR

```{warning}
This section is not fully tested yet.
```

- 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 `train_labels.json`, `val_label.txt` and `test_labels.json` and crop images with the following command:

  ```bash
  python tools/dataset_converters/textrecog/imgur_converter.py PATH/TO/imgur
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── imgur
  │   ├── crops
  │   ├── train_labels.json
  │   ├── test_labels.json
  │   └── val_label.json
  ```

## KAIST

```{warning}
This section is not fully tested yet.
```

- Step1: 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/dataset_converters/common/extract_kaist.py PATH/TO/kaist
  ```

- Step3: Generate `train_labels.json` and `val_label.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
  # Add --preserve-vertical to preserve vertical texts for training, otherwise
  # vertical images will be filtered and stored in PATH/TO/kaist/ignores
  python tools/dataset_converters/textrecog/kaist_converter.py PATH/TO/kaist --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── kaist
  │   ├── crops
  │   ├── ignores
  │   ├── train_labels.json
  │   └── val_label.json (optional)
  ```

## MTWI

```{warning}
This section is not fully tested yet.
```

- 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 `train_labels.json` and `val_label.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
  # Add --preserve-vertical to preserve vertical texts for training, otherwise
  # vertical images will be filtered and stored in PATH/TO/mtwi/ignores
  python tools/dataset_converters/textrecog/mtwi_converter.py PATH/TO/mtwi --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── mtwi
  │   ├── crops
  │   ├── train_labels.json
  │   └── val_label.json (optional)
  ```

## COCO Text v2

```{warning}
This section is not fully tested yet.
```

- 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 `train_labels.json` and `val_label.json` with the following command:

  ```bash
  # Add --preserve-vertical to preserve vertical texts for training, otherwise
  # vertical images will be filtered and stored in PATH/TO/mtwi/ignores
  python tools/dataset_converters/textrecog/cocotext_converter.py PATH/TO/coco_textv2 --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── coco_textv2
  │   ├── crops
  │   ├── ignores
  │   ├── train_labels.json
  │   └── val_label.json
  ```

## ReCTS

```{warning}
This section is not fully tested yet.
```

- 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 -f && rm -rf gt
  ```

- Step2: Generate `train_labels.json` and `val_label.json` (optional) with the 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/dataset_converters/textrecog/rects_converter.py PATH/TO/rects --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── rects
  │   ├── crops
  │   ├── ignores
  │   ├── train_labels.json
  │   └── val_label.json (optional)
  ```

## ILST

```{warning}
This section is not fully tested yet.
```

- Step1: Download `IIIT-ILST.zip` from [onedrive link](https://iiitaphyd-my.sharepoint.com/:f:/g/personal/minesh_mathew_research_iiit_ac_in/EtLvCozBgaBIoqglF4M-lHABMgNcCDW9rJYKKWpeSQEElQ?e=zToXZP)

- Step2: Run the following commands

  ```bash
  unzip -q IIIT-ILST.zip && rm IIIT-ILST.zip
  cd IIIT-ILST

  # rename files
  cd Devanagari && for i in `ls`; do mv -f $i `echo "devanagari_"$i`; done && cd ..
  cd Malayalam && for i in `ls`; do mv -f $i `echo "malayalam_"$i`; done && cd ..
  cd Telugu && for i in `ls`; do mv -f $i `echo "telugu_"$i`; done && cd ..

  # transfer image path
  mkdir imgs && mkdir annotations
  mv Malayalam/{*jpg,*jpeg} imgs/ && mv Malayalam/*xml annotations/
  mv Devanagari/*jpg imgs/ && mv Devanagari/*xml annotations/
  mv Telugu/*jpeg imgs/ && mv Telugu/*xml annotations/

  # remove unnecessary files
  rm -rf Devanagari && rm -rf Malayalam && rm -rf Telugu && rm -rf README.txt
  ```

- Step3: Generate `train_labels.json` and `val_label.json` (optional) and crop images using 4 processes with the following command (add `--preserve-vertical` if you wish to preserve the images containing vertical texts). Since the original dataset doesn't have a validation set, you may specify `--val-ratio` to split the dataset. E.g., if val-ratio is 0.2, then 20% of the data are left out as the validation set in this example.

  ```bash
  python tools/dataset_converters/textrecog/ilst_converter.py PATH/TO/IIIT-ILST --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── IIIT-ILST
  │   ├── crops
  │   ├── ignores
  │   ├── train_labels.json
  │   └── val_label.json (optional)
  ```

## VinText

```{warning}
This section is not fully tested yet.
```

- Step1: Download [vintext.zip](https://drive.google.com/drive/my-drive) to `vintext`

  ```bash
  mkdir vintext && cd vintext

  # Download dataset from google drive
  wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1UUQhNvzgpZy7zXBFQp0Qox-BBjunZ0ml' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1UUQhNvzgpZy7zXBFQp0Qox-BBjunZ0ml" -O vintext.zip && rm -rf /tmp/cookies.txt

  # Extract images and annotations
  unzip -q vintext.zip && rm vintext.zip
  mv vietnamese/labels ./ && mv vietnamese/test_image ./ && mv vietnamese/train_images ./ && mv vietnamese/unseen_test_images ./
  rm -rf vietnamese

  # Rename files
  mv labels annotations && mv test_image test && mv train_images  training && mv unseen_test_images  unseen_test
  mkdir imgs
  mv training imgs/ && mv test imgs/ && mv unseen_test imgs/
  ```

- Step2: Generate `train_labels.json`, `test_labels.json`, `unseen_test_labels.json`,  and crop images using 4 processes with the following command (add `--preserve-vertical` if you wish to preserve the images containing vertical texts).

  ```bash
  python tools/dataset_converters/textrecog/vintext_converter.py PATH/TO/vietnamese --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── vintext
  │   ├── crops
  │   ├── ignores
  │   ├── train_labels.json
  │   ├── test_labels.json
  │   └── unseen_test_labels.json
  ```

## BID

```{warning}
This section is not fully tested yet.
```

- Step1: Download [BID Dataset.zip](https://drive.google.com/file/d/1Oi88TRcpdjZmJ79WDLb9qFlBNG8q2De6/view)

- Step2: Run the following commands to preprocess the dataset

  ```bash
  # Rename
  mv BID\ Dataset.zip BID_Dataset.zip

  # Unzip and Rename
  unzip -q BID_Dataset.zip && rm BID_Dataset.zip
  mv BID\ Dataset BID

  # The BID dataset has a problem of permission, and you may
  # add permission for this file
  chmod -R 777 BID
  cd BID
  mkdir imgs && mkdir annotations

  # For images and annotations
  mv CNH_Aberta/*in.jpg imgs && mv CNH_Aberta/*txt annotations && rm -rf CNH_Aberta
  mv CNH_Frente/*in.jpg imgs && mv CNH_Frente/*txt annotations && rm -rf CNH_Frente
  mv CNH_Verso/*in.jpg imgs && mv CNH_Verso/*txt annotations && rm -rf CNH_Verso
  mv CPF_Frente/*in.jpg imgs && mv CPF_Frente/*txt annotations && rm -rf CPF_Frente
  mv CPF_Verso/*in.jpg imgs && mv CPF_Verso/*txt annotations && rm -rf CPF_Verso
  mv RG_Aberto/*in.jpg imgs && mv RG_Aberto/*txt annotations && rm -rf RG_Aberto
  mv RG_Frente/*in.jpg imgs && mv RG_Frente/*txt annotations && rm -rf RG_Frente
  mv RG_Verso/*in.jpg imgs && mv RG_Verso/*txt annotations && rm -rf RG_Verso

  # Remove unnecessary files
  rm -rf desktop.ini
  ```

- Step3: Generate `train_labels.json` and `val_label.json` (optional) and crop images using 4 processes with the following command (add `--preserve-vertical` if you wish to preserve the images containing vertical texts). Since the original dataset doesn't have a validation set, you may specify `--val-ratio` to split the dataset. E.g., if test-ratio is 0.2, then 20% of the data are left out as the validation set in this example.

  ```bash
  python tools/dataset_converters/textrecog/bid_converter.py PATH/TO/BID --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  ├── BID
  │   ├── crops
  │   ├── ignores
  │   ├── train_labels.json
  │   └── val_label.json (optional)
  ```

## RCTW

```{warning}
This section is not fully tested yet.
```

- Step1: Download `train_images.zip.001`, `train_images.zip.002`, and `train_gts.zip` from the [homepage](https://rctw.vlrlab.net/dataset.html), extract the zips to `rctw/imgs` and `rctw/annotations`, respectively.

- Step2: Generate `train_labels.json` and `val_label.json` (optional). Since the original dataset doesn't have a validation set, you may specify `--val-ratio` to split the dataset. E.g., if val-ratio is 0.2, then 20% of the data are left out as the validation set in this example.

  ```bash
  # Annotations of RCTW 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/rctw/ignores
  python tools/dataset_converters/textrecog/rctw_converter.py PATH/TO/rctw --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  │── rctw
  │   ├── crops
  │   ├── ignores
  │   ├── train_labels.json
  │   └── val_label.json (optional)
  ```

## HierText

```{warning}
This section is not fully tested yet.
```

- Step1 (optional): Install [AWS CLI](https://mmocr.readthedocs.io/en/latest/datasets/recog.html#install-aws-cli-optional).

- Step2: Clone [HierText](https://github.com/google-research-datasets/hiertext) repo to get annotations

  ```bash
  mkdir HierText
  git clone https://github.com/google-research-datasets/hiertext.git
  ```

- Step3: Download `train.tgz`, `validation.tgz` from aws

  ```bash
  aws s3 --no-sign-request cp s3://open-images-dataset/ocr/train.tgz .
  aws s3 --no-sign-request cp s3://open-images-dataset/ocr/validation.tgz .
  ```

- Step4: Process raw data

  ```bash
  # process annotations
  mv hiertext/gt ./
  rm -rf hiertext
  mv gt annotations
  gzip -d annotations/train.json.gz
  gzip -d annotations/validation.json.gz
  # process images
  mkdir imgs
  mv train.tgz imgs/
  mv validation.tgz imgs/
  tar -xzvf imgs/train.tgz
  tar -xzvf imgs/validation.tgz
  ```

- Step5: Generate `train_labels.json` and `val_label.json`. HierText includes different levels of annotation, including `paragraph`, `line`, and `word`. Check the original [paper](https://arxiv.org/pdf/2203.15143.pdf) for details. E.g. set `--level paragraph` to get paragraph-level annotation. Set `--level line` to get line-level annotation. set `--level word` to get word-level annotation.

  ```bash
  # Collect word annotation from HierText  --level word
  # Add --preserve-vertical to preserve vertical texts for training, otherwise vertical images will be filtered and stored in PATH/TO/HierText/ignores
  python tools/dataset_converters/textrecog/hiertext_converter.py PATH/TO/HierText --level word --nproc 4
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  │── HierText
  │   ├── crops
  │   ├── ignores
  │   ├── train_labels.json
  │   └── val_label.json
  ```

## ArT

```{warning}
This section is not fully tested yet.
```

- Step1: Download `train_images.tar.gz`, and `train_labels.json` from the [homepage](https://rrc.cvc.uab.es/?ch=14&com=downloads) to `art/`

  ```bash
  mkdir art && cd art
  mkdir annotations

  # Download ArT dataset
  wget https://dataset-bj.cdn.bcebos.com/art/train_task2_images.tar.gz
  wget https://dataset-bj.cdn.bcebos.com/art/train_task2_labels.json

  # Extract
  tar -xf train_task2_images.tar.gz
  mv train_task2_images crops
  mv train_task2_labels.json annotations/

  # Remove unnecessary files
  rm train_images.tar.gz
  ```

- Step2: Generate `train_labels.json` and `val_label.json` (optional). Since the test annotations are not publicly available, you may specify `--val-ratio` to split the dataset. E.g., if val-ratio is 0.2, then 20% of the data are left out as the validation set in this example.

  ```bash
  # Annotations of ArT test split is not publicly available, split a validation set by adding --val-ratio 0.2
  python tools/dataset_converters/textrecog/art_converter.py PATH/TO/art
  ```

- After running the above codes, the directory structure should be as follows:

  ```text
  │── art
  │   ├── crops
  │   ├── train_labels.json
  │   └── val_label.json (optional)
  ```
