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Text Recognition

Overview

The structure of the text recognition dataset directory is organized as follows.

├── mixture
│   ├── coco_text
│   │   ├── train_label.txt
│   │   ├── train_words
│   ├── icdar_2011
│   │   ├── training_label.txt
│   │   ├── Challenge1_Training_Task3_Images_GT
│   ├── icdar_2013
│   │   ├── train_label.txt
│   │   ├── test_label_1015.txt
│   │   ├── test_label_1095.txt
│   │   ├── Challenge2_Training_Task3_Images_GT
│   │   ├── Challenge2_Test_Task3_Images
│   ├── icdar_2015
│   │   ├── train_label.txt
│   │   ├── test_label.txt
│   │   ├── ch4_training_word_images_gt
│   │   ├── ch4_test_word_images_gt
│   ├── III5K
│   │   ├── train_label.txt
│   │   ├── test_label.txt
│   │   ├── train
│   │   ├── test
│   ├── ct80
│   │   ├── test_label.txt
│   │   ├── image
│   ├── svt
│   │   ├── test_label.txt
│   │   ├── image
│   ├── svtp
│   │   ├── test_label.txt
│   │   ├── image
│   ├── Syn90k
│   │   ├── shuffle_labels.txt
│   │   ├── label.txt
│   │   ├── label.lmdb
│   │   ├── mnt
│   ├── SynthText
│   │   ├── alphanumeric_labels.txt
│   │   ├── shuffle_labels.txt
│   │   ├── instances_train.txt
│   │   ├── label.txt
│   │   ├── label.lmdb
│   │   ├── synthtext
│   ├── SynthAdd
│   │   ├── label.txt
│   │   ├── label.lmdb
│   │   ├── SynthText_Add
│   ├── TextOCR
│   │   ├── image
│   │   ├── train_label.txt
│   │   ├── val_label.txt
│   ├── Totaltext
│   │   ├── imgs
│   │   ├── annotations
│   │   ├── train_label.txt
│   │   ├── test_label.txt
│   ├── 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
│   ├── funsd
│   │   ├── imgs
│   │   ├── dst_imgs
│   │   ├── annotations
│   │   ├── train_label.txt
│   │   ├── test_label.txt

Dataset	images	annotation file	annotation file
		training	test
coco_text	homepage	train_label.txt	-
icdar_2011	homepage	train_label.txt	-
icdar_2013	homepage	train_label.txt	test_label_1015.txt
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	-	-

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

Preparation Steps

ICDAR 2013

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

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

IIIT5K

Step1: Download IIIT5K-Word_V3.0.tar.gz from homepage
Step2: Download train_label.txt and test_label.txt

svt

Step1: Download svt.zip form homepage
Step2: Download test_label.txt
Step3:

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

ct80

Step1: Download test_label.txt

svtp

Step1: Download test_label.txt

coco_text

Step1: Download from homepage
Step2: Download train_label.txt

MJSynth (Syn90k)

Step1: Download mjsynth.tar.gz from homepage
Step2: Download label.txt (8,919,273 annotations) and shuffle_labels.txt (2,400,000 randomly sampled annotations). 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

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).

:::{warning} 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

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

:::{tip} To convert label file with txt format to lmdb format,

python tools/data/utils/txt2lmdb.py -i <txt_label_path> -o <lmdb_label_path>

For example,

python tools/data/utils/txt2lmdb.py -i data/mixture/Syn90k/label.txt -o data/mixture/Syn90k/label.lmdb

:::

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

Totaltext

Step1: Download totaltext.zip from github dataset and groundtruth_text.zip 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 annotations
unzip groundtruth_text.zip
cd Groundtruth
mv Polygon/Train ../annotations/training
mv Polygon/Test ../annotations/test

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 -o /path/to/totaltext --split-list training test

OpenVINO

Step0: Install awscli.
Step1: 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

Step2: 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

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