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Text Detection 

This page is a manual preparation guide for datasets not yet supported by [Dataset Preparer](./dataset_preparer.md), which all these scripts will be eventually migrated into.

Overview

Dataset Images Annotation Files
training validation testing
ICDAR2011 homepage - -
ICDAR2017 homepage instances_training.json instances_val.json -
CurvedSynText150k homepage | Part1 | Part2 instances_training.json - -
DeText homepage - - -
Lecture Video DB homepage - - -
LSVT homepage - - -
IMGUR homepage - - -
KAIST homepage - - -
MTWI 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]

For users in China, these datasets can also be downloaded from OpenDataLab with high speed:

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)

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 2017

  • Follow similar steps as ICDAR 2015.

  • The resulting directory structure looks like the following:

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

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

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)

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)