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Text Detection[Deprecated]

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 Files
training validation testing
CTW1500 homepage - - -
ICDAR2011 homepage - -
ICDAR2013 homepage - - -
ICDAR2015 homepage instances_training.json - instances_test.json
ICDAR2017 homepage instances_training.json instances_val.json -
Synthtext homepage instances_training.lmdb (data.mdb, lock.mdb) - -
TextOCR homepage - - -
Totaltext homepage - - -
CurvedSynText150k homepage | Part1 | Part2 instances_training.json - -
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 - - -

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]

Important Note

**For users who want to train models on CTW1500, ICDAR 2015/2017, and Totaltext dataset,** there might be some images containing orientation info in EXIF data. The default OpenCV
backend used in MMCV would read them and apply the rotation on the images.  However, their gold annotations are made on the raw pixels, and such
inconsistency results in false examples in the training set. Therefore, users should use `dict(type='LoadImageFromFile', color_type='color_ignore_orientation')` in pipelines to change MMCV's default loading behaviour. (see [DBNet's pipeline config](https://github.com/open-mmlab/mmocr/blob/main/configs/_base_/det_pipelines/dbnet_pipeline.py) for example)

CTW1500

  • Step0: Read Important Note

  • Step1: Download train_images.zip, test_images.zip, train_labels.zip, test_labels.zip from github

    mkdir ctw1500 && cd ctw1500
mkdir imgs && mkdir annotations

# For annotations
cd annotations
wget -O train_labels.zip https://universityofadelaide.box.com/shared/static/jikuazluzyj4lq6umzei7m2ppmt3afyw.zip
wget -O test_labels.zip https://cloudstor.aarnet.edu.au/plus/s/uoeFl0pCN9BOCN5/download
unzip train_labels.zip && mv ctw1500_train_labels training
unzip test_labels.zip -d test
cd ..
# For images
cd imgs
wget -O train_images.zip https://universityofadelaide.box.com/shared/static/py5uwlfyyytbb2pxzq9czvu6fuqbjdh8.zip
wget -O test_images.zip https://universityofadelaide.box.com/shared/static/t4w48ofnqkdw7jyc4t11nsukoeqk9c3d.zip
unzip train_images.zip && mv train_images training
unzip test_images.zip && mv test_images test

  • Step2: Generate instances_training.json and instances_test.json with following command:

    python tools/dataset_converters/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1500 --split-list training test

  • The resulting directory structure looks like the following:

    ├── ctw1500
│   ├── imgs
│   ├── annotations
│   ├── instances_training.json
│   └── instances_val.json

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 Task 1.1: Text Localization (2013 edition).

    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:

    python tools/dataset_converters/textdet/ic11_converter.py PATH/TO/icdar2011 --nproc 4

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

    │── 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 Task 2.1: Text Localization (2013 edition).

    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:

    python tools/dataset_converters/textdet/ic13_converter.py PATH/TO/icdar2013 --nproc 4

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

    │── icdar2013
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json

ICDAR 2015

  • Step0: Read 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

  • Step2:

    mkdir icdar2015 && cd icdar2015
mkdir imgs && mkdir annotations
# For images,
mv ch4_training_images imgs/training
mv ch4_test_images imgs/test
# For annotations,
mv ch4_training_localization_transcription_gt annotations/training
mv Challenge4_Test_Task1_GT annotations/test

  • Step3: Download instances_training.json and instances_test.json and move them to icdar2015

  • Or, generate instances_training.json and instances_test.json with the following command:

    python tools/dataset_converters/textdet/icdar_converter.py /path/to/icdar2015 -o /path/to/icdar2015 -d icdar2015 --split-list training test

  • The resulting directory structure looks like the following:

    ├── icdar2015
│   ├── imgs
│   ├── annotations
│   ├── instances_test.json
│   └── instances_training.json

ICDAR 2017

  • Follow similar steps as ICDAR 2015.

  • The resulting directory structure looks like the following:

    ├── icdar2017
│   ├── imgs
│   ├── annotations
│   ├── instances_training.json
│   └── instances_val.json

SynthText

  • Step1: Download SynthText.zip from [homepage](https://www.robots.ox.ac.uk/~vgg/data/scenetext/ and extract its content to synthtext/img.

  • Step2: Download data.mdb and lock.mdb to synthtext/instances_training.lmdb/.

  • The resulting directory structure looks like the following:

    ├── synthtext
│   ├── imgs
│   └── instances_training.lmdb
│       ├── data.mdb
│       └── lock.mdb

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 instances_training.json and instances_val.json with the following command:

    python tools/dataset_converters/textdet/textocr_converter.py /path/to/textocr

  • The resulting directory structure looks like the following:

    ├── textocr
│   ├── train
│   ├── instances_training.json
│   └── instances_val.json

Totaltext

  • Step0: Read Important Note

  • 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 instances_training.json and instances_test.json with the following command:

    python tools/dataset_converters/textdet/totaltext_converter.py /path/to/totaltext

  • The resulting directory structure looks like the following:

    ├── totaltext
│   ├── imgs
│   ├── annotations
│   ├── instances_test.json
│   └── instances_training.json

CurvedSynText150k

  • Step1: Download syntext1.zip and syntext2.zip to CurvedSynText150k/.

  • Step2:

    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 to CurvedSynText150k/

  • Or, generate instances_training.json with following command:

    python tools/dataset_converters/common/curvedsyntext_converter.py PATH/TO/CurvedSynText150k --nproc 4

  • The resulting directory structure looks like the following:

    ├── CurvedSynText150k
│   ├── syntext_word_eng
│   ├── emcs_imgs
│   └── instances_training.json

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 instances_training.json and instances_test.json with following command:

    python tools/dataset_converters/textdet/funsd_converter.py PATH/TO/funsd --nproc 4

  • The resulting directory structure looks like the following:

    │── funsd
│   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.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.

    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:

    python tools/dataset_converters/textdet/detext_converter.py PATH/TO/detext --nproc 4

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

    │── detext
│   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json

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 instances_training.json, instances_val.json, and instances_test.json with following command:

    python tools/dataset_converters/textdet/naf_converter.py PATH/TO/naf --nproc 4

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

    │── naf
│   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   ├── instances_val.json
│   └── instances_training.json

SROIE

  • 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 instances_training.json and instances_test.json with the following command:

    python tools/dataset_converters/textdet/sroie_converter.py PATH/TO/sroie --nproc 4

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

    ├── sroie
│   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json

Lecture Video DB

  • 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

mv IIIT-CVid/Frames imgs

rm IIIT-CVid.zip

  • Step2: Generate instances_training.json, instances_val.json, and instances_test.json with following command:

    python tools/dataset_converters/textdet/lv_converter.py PATH/TO/lv --nproc 4

  • The resulting directory structure looks like the following:

    │── lv
│   ├── imgs
│   ├── instances_test.json
│   ├── instances_training.json
│   └── instances_val.json

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 instances_training.json and instances_val.json (optional) with the following command:

    # Annotations of LSVT test split is not publicly available, split a validation
# set by adding --val-ratio 0.2
python tools/dataset_converters/textdet/lsvt_converter.py PATH/TO/lsvt

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

    |── lsvt
│   ├── imgs
│   ├── instances_training.json
│   └── instances_val.json (optional)

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 instances_train.json, instance_val.json and instances_test.json with the following command:

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

  • Step3: Generate instances_training.json and instances_val.json (optional) with following command:

    # Since KAIST does not provide an official split, you can split the dataset by adding --val-ratio 0.2
python tools/dataset_converters/textdet/kaist_converter.py PATH/TO/kaist --nproc 4

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

    │── kaist
│   ├── annotations
│   ├── imgs
│   ├── instances_training.json
│   └── instances_val.json (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 instances_training.json and instance_val.json (optional) with the following command:

    # Annotations of MTWI test split is not publicly available, split a validation
# set by adding --val-ratio 0.2
python tools/dataset_converters/textdet/mtwi_converter.py PATH/TO/mtwi --nproc 4

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

    │── mtwi
│   ├── annotations
│   ├── imgs
│   ├── instances_training.json
│   └── instances_val.json (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 instances_training.json and instances_val.json with the following command:

    python tools/dataset_converters/textdet/cocotext_converter.py PATH/TO/coco_textv2

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

    │── coco_textv2
│   ├── annotations
│   ├── imgs
│   ├── instances_training.json
│   └── instances_val.json

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 && rm -rf gt

  • Step2: Generate instances_training.json and instances_val.json (optional) with following command:

    # Annotations of ReCTS test split is not publicly available, split a validation
# set by adding --val-ratio 0.2
python tools/dataset_converters/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:

    │── rects
│   ├── annotations
│   ├── imgs
│   ├── instances_val.json (optional)
│   └── instances_training.json

ILST

  • Step1: Download IIIT-ILST from onedrive

  • 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 instances_training.json and instances_val.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.

    python tools/dataset_converters/textdet/ilst_converter.py    PATH/TO/IIIT-ILST --nproc 4

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

    │── IIIT-ILST
│   ├── annotations
│   ├── imgs
│   ├── instances_val.json (optional)
│   └── instances_training.json

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 instances_training.json, instances_test.json and instances_unseen_test.json

    python tools/dataset_converters/textdet/vintext_converter.py PATH/TO/vintext --nproc 4

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

    │── vintext
│   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   ├── instances_unseen_test.json
│   └── instances_training.json

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: - Step3: Generate instances_training.json and instances_val.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.

    python tools/dataset_converters/textdet/bid_converter.py PATH/TO/BID --nproc 4

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

    │── BID
│   ├── annotations
│   ├── imgs
│   ├── instances_training.json
│   └── instances_val.json (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 instances_training.json and instances_val.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.

    # Annotations of RCTW test split is not publicly available, split a validation set by adding --val-ratio 0.2
python tools/dataset_converters/textdet/rctw_converter.py PATH/TO/rctw --nproc 4

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

    │── rctw
│   ├── annotations
│   ├── imgs
│   ├── instances_training.json
│   └── instances_val.json (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 instances_training.json and instance_val.json. HierText includes different levels of annotation, from paragraph, line, to 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
python tools/dataset_converters/textdet/hiertext_converter.py PATH/TO/HierText --level word --nproc 4

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

    │── HierText
│   ├── annotations
│   ├── imgs
│   ├── instances_training.json
│   └── instances_val.json

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_images.tar.gz --no-check-certificate
wget https://dataset-bj.cdn.bcebos.com/art/train_labels.json --no-check-certificate

# Extract
tar -xf train_images.tar.gz
mv train_images imgs
mv train_labels.json annotations/

# Remove unnecessary files
rm train_images.tar.gz

  • Step2: Generate instances_training.json and instances_val.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.

    # Annotations of ArT test split is not publicly available, split a validation set by adding --val-ratio 0.2
python tools/data/textdet/art_converter.py PATH/TO/art --nproc 4

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

    │── art
│   ├── annotations
│   ├── imgs
│   ├── instances_training.json
│   └── instances_val.json (optional)