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Text Recognition

Overview

Dataset images annotation file annotation file
training test
coco_text homepage train_label.txt -
ICDAR2011 homepage - -
ICDAR2013 homepage - -
icdar_2015 homepage train_label.txt test_label.txt
IIIT5K homepage train_label.txt test_label.txt
ct80 homepage - test_label.txt
svt homepage - test_label.txt
svtp unofficial homepage[1] - test_label.txt
MJSynth (Syn90k) homepage shuffle_labels.txt | label.txt -
SynthText (Synth800k) homepage alphanumeric_labels.txt |shuffle_labels.txt | instances_train.txt | label.txt -
SynthAdd SynthText_Add.zip (code:627x) label.txt -
TextOCR homepage - -
Totaltext homepage - -
OpenVINO Open Images annotations annotations
FUNSD homepage - -
DeText homepage - -
NAF homepage - -
SROIE homepage - -
Lecture Video DB homepage - -
LSVT homepage - -
IMGUR homepage - -
KAIST homepage - -
MTWI homepage - -
COCO Text v2 homepage - -
ReCTS homepage - -
IIIT-ILST homepage - -
VinText homepage - -
BID homepage - -
RCTW homepage - -
HierText homepage - -
ArT homepage - -

(*) 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 to be installed in advance, we provide a quick installation guide here:

      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 Task 1.3: Word Recognition (2013 edition).

    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 train/gt.txt annotations/Challenge1_Train_Task3_GT.txt

  • Step2: Convert original annotations to Train_label.jsonl and Test_label.jsonl with the following command:

    python tools/data/textrecog/ic11_converter.py PATH/TO/icdar2011

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

    ├── icdar2011
│   ├── crops
│   ├── train_label.jsonl
│   └── test_label.jsonl

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 Task 2.3: Word Recognition (2013 edition).

    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

# 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

rm Challenge2_Training_Task3_Images_GT.zip && rm Challenge2_Test_Task3_Images.zip

  • Step 2: Generate Train_label.jsonl and Test_label.jsonl with the following command:

    python tools/data/textrecog/ic13_converter.py PATH/TO/icdar2013

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

    ├── icdar2013
│   ├── crops
│   ├── train_label.jsonl
│   └── test_label.jsonl

ICDAR 2013 [Deprecated]

  • Step1: Download Challenge2_Test_Task3_Images.zip and Challenge2_Training_Task3_Images_GT.zip from homepage

  • Step2: Download test_label_1015.txt and train_label.txt

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

    ├── icdar_2013
│   ├── train_label.txt
│   ├── test_label_1015.txt
│   ├── test_label_1095.txt
│   ├── Challenge2_Training_Task3_Images_GT
│   └──  Challenge2_Test_Task3_Images

ICDAR 2015

  • Step1: Download ch4_training_word_images_gt.zip and ch4_test_word_images_gt.zip from homepage

  • Step2: Download train_label.txt and test_label.txt

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

    ├── icdar_2015
│   ├── train_label.txt
│   ├── test_label.txt
│   ├── ch4_training_word_images_gt
│   └── ch4_test_word_images_gt

IIIT5K

  • Step1: Download IIIT5K-Word_V3.0.tar.gz from homepage

  • Step2: Download train_label.txt and test_label.txt

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

    ├── III5K
│   ├── train_label.txt
│   ├── test_label.txt
│   ├── train
│   └── test

svt

  • Step1: Download svt.zip form homepage

  • Step2: Download test_label.txt

  • Step3:

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

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

    ├── svt
│   ├── test_label.txt
│   └── image

ct80

  • Step1: Download test_label.txt

  • Step2: Download timage.tar.gz

  • Step3:

    mkdir ct80 && cd ct80
mv /path/to/test_label.txt .
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:

    ├── ct80
│   ├── test_label.txt
│   └── timage

svtp

  • Step1: Download test_label.txt

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

    ├── svtp
│   ├── test_label.txt
│   └── image

coco_text

  • Step1: Download from homepage

  • Step2: Download train_label.txt

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

    ├── coco_text
│   ├── train_label.txt
│   └── train_words

MJSynth (Syn90k)

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

  • Step3:

    mkdir Syn90k && cd Syn90k

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

tar -xzf mjsynth.tar.gz

mv /path/to/shuffle_labels.txt .
mv /path/to/label.txt .

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

ln -s /path/to/Syn90k Syn90k

# Convert 'txt' format annos to 'lmdb' (optional)
cd /path/to/mmocr
python tools/data/utils/lmdb_converter.py data/mixture/Syn90k/label.txt data/mixture/Syn90k/label.lmdb --label-only

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

    ├── Syn90k
│   ├── shuffle_labels.txt
│   ├── label.txt
│   ├── label.lmdb (optional)
│   └── mnt

SynthText (Synth800k)

  • Step1: Download SynthText.zip from homepage

  • Step2: According to your actual needs, download the most appropriate one from the following options: label.txt (7,266,686 annotations), shuffle_labels.txt (2,400,000 randomly sampled annotations), alphanumeric_labels.txt (7,239,272 annotations with alphanumeric characters only) and instances_train.txt (7,266,686 character-level annotations).

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

  • Step3:

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

mv /path/to/shuffle_labels.txt .
mv /path/to/label.txt .
mv /path/to/alphanumeric_labels.txt .
mv /path/to/instances_train.txt .

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

  • Step4: Generate cropped images and labels:

    cd /path/to/mmocr

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

# Convert 'txt' format annos to 'lmdb' (optional)
cd /path/to/mmocr
python tools/data/utils/lmdb_converter.py data/mixture/SynthText/label.txt data/mixture/SynthText/label.lmdb --label-only

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

    ├── SynthText
│   ├── alphanumeric_labels.txt
│   ├── shuffle_labels.txt
│   ├── instances_train.txt
│   ├── label.txt
│   ├── label.lmdb (optional)
│   └── synthtext

SynthAdd

  • Step1: Download SynthText_Add.zip from SynthAdd (code:627x))

  • Step2: Download label.txt

  • Step3:

    mkdir SynthAdd && cd SynthAdd

mv /path/to/SynthText_Add.zip .

unzip SynthText_Add.zip

mv /path/to/label.txt .

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

ln -s /path/to/SynthAdd SynthAdd

# Convert 'txt' format annos to 'lmdb' (optional)
cd /path/to/mmocr
python tools/data/utils/lmdb_converter.py data/mixture/SynthAdd/label.txt data/mixture/SynthAdd/label.lmdb --label-only

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

    ├── SynthAdd
│   ├── label.txt
│   ├── label.lmdb (optional)
│   └── SynthText_Add

To convert label file from `txt` format to `lmdb` format,

```bash
python tools/data/utils/lmdb_converter.py <txt_label_path> <lmdb_label_path> --label-only
```

For example,

```bash
python tools/data/utils/lmdb_converter.py data/mixture/Syn90k/label.txt data/mixture/Syn90k/label.lmdb --label-only
```

TextOCR

  • Step1: Download train_val_images.zip, TextOCR_0.1_train.json and TextOCR_0.1_val.json to textocr/.

    mkdir textocr && cd textocr

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

# For images
unzip -q train_val_images.zip
mv train_images train

  • Step2: Generate train_label.txt, val_label.txt and crop images using 4 processes with the following command:

    python tools/data/textrecog/textocr_converter.py /path/to/textocr 4

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

    ├── TextOCR
│   ├── image
│   ├── train_label.txt
│   └── val_label.txt

Totaltext

  • Step1: Download totaltext.zip from github 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 (Our totaltext_converter.py supports groundtruth with both .mat and .txt format).

    mkdir totaltext && cd totaltext
mkdir imgs && mkdir annotations

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

# For 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_label.txt and test_label.txt with the following command (the cropped images will be saved to data/totaltext/dst_imgs/):

    python tools/data/textrecog/totaltext_converter.py /path/to/totaltext

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

    ├── totaltext
│   ├── dst_imgs
│   ├── train_label.txt
│   └── test_label.txt

OpenVINO

  • Step1 (optional): Install AWS CLI.

  • Step2: Download Open Images subsets train_1, train_2, train_5, train_f, and validation to openvino/.

    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}_label.txt, val_label.txt and crop images using 4 processes with the following command:

    python tools/data/textrecog/openvino_converter.py /path/to/openvino 4

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

    ├── OpenVINO
│   ├── image_1
│   ├── image_2
│   ├── image_5
│   ├── image_f
│   ├── image_val
│   ├── train_1_label.txt
│   ├── train_2_label.txt
│   ├── train_5_label.txt
│   ├── train_f_label.txt
│   └── val_label.txt

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.

    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:

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

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

    ├── detext
│   ├── crops
│   ├── ignores
│   ├── train_label.jsonl
│   └── test_label.jsonl

NAF

  • Step1: Download labeled_images.tar.gz to naf/.

    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_label.txt, val_label.txt, and test_label.txt with following command:

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

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

    ├── naf
│   ├── crops
│   ├── train_label.txt
│   ├── val_label.txt
│   └── test_label.txt

SROIE

  • Step1: Step1: Download 0325updated.task1train(626p).zip, task1&2_test(361p).zip, and text.task1&2-test（361p).zip from homepage to sroie/

  • Step2:

    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_label.jsonl and test_label.jsonl and crop images using 4 processes with the following command:

    python tools/data/textrecog/sroie_converter.py PATH/TO/sroie --nproc 4

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

    ├── sroie
│   ├── crops
│   ├── train_label.jsonl
│   └── test_label.jsonl

Lecture Video DB

The LV dataset has already provided cropped images and the corresponding annotations

  • Step1: Download IIIT-CVid.zip to lv/.

    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_label.txt && mv IIIT-CVid/val.txt val_label.txt && mv IIIT-CVid/test.txt test_label.txt

rm IIIT-CVid.zip

  • Step2: Generate train_label.jsonl, val.jsonl, and test.jsonl with following command:

    python tools/data/textdreog/lv_converter.py PATH/TO/lv

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

    ├── lv
│   ├── Crops
│   ├── train_label.jsonl
│   └── test_label.jsonl

LSVT

  • Step1: Download train_full_images_0.tar.gz, train_full_images_1.tar.gz, and train_full_labels.json to lsvt/.

    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_label.jsonl and val_label.jsonl (optional) with the following command:

    # 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/data/textdrecog/lsvt_converter.py PATH/TO/lsvt --nproc 4

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

    ├── lsvt
│   ├── crops
│   ├── ignores
│   ├── train_label.jsonl
│   └── val_label.jsonl (optional)

FUNSD

  • Step1: Download dataset.zip to funsd/.

    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_label.txt and test_label.txt and crop images using 4 processes with following command (add --preserve-vertical if you wish to preserve the images containing vertical texts):

    python tools/data/textrecog/funsd_converter.py PATH/TO/funsd --nproc 4

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

    ├── funsd
│   ├── imgs
│   ├── dst_imgs
│   ├── annotations
│   ├── train_label.txt
│   └── test_label.txt

IMGUR

  • Step1: Run download_imgur5k.py to download images. You can merge PR#5 in your local repository to enable a much faster parallel execution of image download.

    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_label.txt, val_label.txt and test_label.txt and crop images with the following command:

    python tools/data/textrecog/imgur_converter.py PATH/TO/imgur

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

    ├── imgur
│   ├── crops
│   ├── train_label.jsonl
│   ├── test_label.jsonl
│   └── val_label.jsonl

KAIST

  • Step1: Complete download KAIST_all.zip to kaist/.

    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:

    python tools/data/common/extract_kaist.py PATH/TO/kaist

  • Step3: Generate train_label.jsonl and val_label.jsonl (optional) with following command:

    # 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/data/textrecog/kaist_converter.py PATH/TO/kaist --nproc 4

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

    ├── kaist
│   ├── crops
│   ├── ignores
│   ├── train_label.jsonl
│   └── val_label.jsonl (optional)

MTWI

  • Step1: Download mtwi_2018_train.zip from homepage.

    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_label.jsonl and val_label.jsonl (optional) with the following command:

    # 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/data/textrecog/mtwi_converter.py PATH/TO/mtwi --nproc 4

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

    ├── mtwi
│   ├── crops
│   ├── train_label.jsonl
│   └── val_label.jsonl (optional)

COCO Text v2

  • Step1: Download image train2014.zip and annotation cocotext.v2.zip to coco_textv2/.

    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_label.jsonl and val_label.jsonl with the following command:

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

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

    ├── coco_textv2
│   ├── crops
│   ├── ignores
│   ├── train_label.jsonl
│   └── val_label.jsonl

ReCTS

  • Step1: Download ReCTS.zip to rects/ from the homepage.

    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_label.jsonl and val_label.jsonl (optional) with the following command:

    # 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/textrecog/rects_converter.py PATH/TO/rects --nproc 4

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

    ├── rects
│   ├── crops
│   ├── ignores
│   ├── train_label.jsonl
│   └── val_label.jsonl (optional)

ILST

  • Step1: Download IIIT-ILST.zip from onedrive link

  • Step2: Run the following commands

    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_label.jsonl and val_label.jsonl (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.

    python tools/data/textrecog/ilst_converter.py PATH/TO/IIIT-ILST --nproc 4

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

    ├── IIIT-ILST
│   ├── crops
│   ├── ignores
│   ├── train_label.jsonl
│   └── val_label.jsonl (optional)

VinText

  • Step1: Download vintext.zip to vintext

    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_label.jsonl, test_label.jsonl, unseen_test_label.jsonl, and crop images using 4 processes with the following command (add --preserve-vertical if you wish to preserve the images containing vertical texts).

    python tools/data/textrecog/vintext_converter.py PATH/TO/vietnamese --nproc 4

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

    ├── vintext
│   ├── crops
│   ├── ignores
│   ├── train_label.jsonl
│   ├── test_label.jsonl
│   └── unseen_test_label.jsonl

BID

  • Step1: Download BID Dataset.zip

  • Step2: Run the following commands to preprocess the dataset

    # 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_label.jsonl and val_label.jsonl (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.

    python tools/data/textrecog/bid_converter.py dPATH/TO/BID --nproc 4

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

    ├── BID
│   ├── crops
│   ├── ignores
│   ├── train_label.jsonl
│   └── val_label.jsonl (optional)

RCTW

  • Step1: Download train_images.zip.001, train_images.zip.002, and train_gts.zip from the homepage, extract the zips to rctw/imgs and rctw/annotations, respectively.

  • Step2: Generate train_label.jsonl and val_label.jsonl (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.

    # 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/data/textrecog/rctw_converter.py PATH/TO/rctw --nproc 4

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

    │── rctw
│   ├── crops
│   ├── ignores
│   ├── train_label.jsonl
│   └── val_label.jsonl (optional)

HierText

  • Step1 (optional): Install AWS CLI.

  • Step2: Clone HierText repo to get annotations

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

  • Step3: Download train.tgz, validation.tgz from aws

    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

    # process annotations
mv hiertext/gt ./
rm -rf hiertext
mv gt annotations
gzip -d annotations/train.jsonl.gz
gzip -d annotations/validation.jsonl.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_label.jsonl and val_label.jsonl. HierText includes different levels of annotation, including paragraph, line, and word. Check the original paper 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.

    # 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/data/textrecog/hiertext_converter.py PATH/TO/HierText --level word --nproc 4

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

    │── HierText
│   ├── crops
│   ├── ignores
│   ├── train_label.jsonl
│   └── val_label.jsonl

ArT

  • Step1: Download train_images.tar.gz, and train_labels.json from the homepage to art/

    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_label.jsonl and val_label.jsonl (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.

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

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

    │── art
│   ├── crops
│   ├── train_label.jsonl
│   └── val_label.jsonl (optional)