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# Text Recognition
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## Overview
**The structure of the text recognition dataset directory is organized as follows.**
```text
├── 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
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│ │ ├── alphanumeric_labels.txt
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│ │ ├── 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
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│ ├── 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
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│ ├── funsd
│ │ ├── imgs
│ │ ├── dst_imgs
│ │ ├── annotations
│ │ ├── train_label.txt
│ │ ├── test_label.txt
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│ ├── lv
│ │ ├── Crops
│ │ ├── train_label.jsonl
│ │ ├── test_label.jsonl
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```
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| Dataset | images | annotation file | annotation file |
| :-------------------: | :---------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------: |
| | | training | test |
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| coco_text | [homepage ](https://rrc.cvc.uab.es/?ch=5&com=downloads ) | [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/coco_text/train_label.txt ) | - | |
| icdar_2011 | [homepage ](http://www.cvc.uab.es/icdar2011competition/?com=downloads ) | [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/train_label.txt ) | - | |
| icdar_2013 | [homepage ](https://rrc.cvc.uab.es/?ch=2&com=downloads ) | [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/train_label.txt ) | [test_label_1015.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/test_label_1015.txt ) | |
| icdar_2015 | [homepage ](https://rrc.cvc.uab.es/?ch=4&com=downloads ) | [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/train_label.txt ) | [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/test_label.txt ) | |
| IIIT5K | [homepage ](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html ) | [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/train_label.txt ) | [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/test_label.txt ) | |
| ct80 | [homepage ](http://cs-chan.com/downloads_CUTE80_dataset.html ) | - | [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/ct80/test_label.txt ) | |
| svt | [homepage ](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset ) | - | [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/svt/test_label.txt ) | |
| svtp | [unofficial homepage\[1\]](https://github.com/Jyouhou/Case-Sensitive-Scene-Text-Recognition-Datasets) | - | [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/svtp/test_label.txt ) | |
| MJSynth (Syn90k) | [homepage ](https://www.robots.ox.ac.uk/~vgg/data/text/ ) | [shuffle_labels.txt ](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/shuffle_labels.txt ) \| [label.txt ](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/label.txt ) | - | |
| SynthText (Synth800k) | [homepage ](https://www.robots.ox.ac.uk/~vgg/data/scenetext/ ) | [alphanumeric_labels.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthText/alphanumeric_labels.txt ) \|[shuffle_labels.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthText/shuffle_labels.txt ) \| [instances_train.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthText/instances_train.txt ) \| [label.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthText/label.txt ) | - | |
| SynthAdd | [SynthText_Add.zip ](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg ) (code:627x) | [label.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthAdd/label.txt ) | - | |
| 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/ ) | - | - | |
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| SROIE | [homepage ](https://rrc.cvc.uab.es/?ch=13 ) | - | - | - |
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| Lecture Video DB | [homepage ](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb ) | - | - | - |
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(*) 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 ](https://rrc.cvc.uab.es/?ch=2&com=downloads )
- Step2: Download [test_label_1015.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/test_label_1015.txt ) and [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/train_label.txt )
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### ICDAR 2015
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- 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_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/train_label.txt ) and [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/test_label.txt )
### IIIT5K
- Step1: Download `IIIT5K-Word_V3.0.tar.gz` from [homepage ](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html )
- Step2: Download [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/train_label.txt ) and [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/test_label.txt )
### svt
- Step1: Download `svt.zip` form [homepage ](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset )
- Step2: Download [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/svt/test_label.txt )
- Step3:
```bash
python tools/data/textrecog/svt_converter.py < download_svt_dir_path >
```
### ct80
- Step1: Download [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/ct80/test_label.txt )
### svtp
- Step1: Download [test_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/svtp/test_label.txt )
### coco_text
- Step1: Download from [homepage ](https://rrc.cvc.uab.es/?ch=5&com=downloads )
- Step2: Download [train_label.txt ](https://download.openmmlab.com/mmocr/data/mixture/coco_text/train_label.txt )
### MJSynth (Syn90k)
- Step1: Download `mjsynth.tar.gz` from [homepage ](https://www.robots.ox.ac.uk/~vgg/data/text/ )
- Step2: Download [label.txt ](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/label.txt ) (8,919,273 annotations) and [shuffle_labels.txt ](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/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:
```bash
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 ](https://www.robots.ox.ac.uk/~vgg/data/scenetext/ )
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- Step2: According to your actual needs, download the most appropriate one from the following options: [label.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthText/label.txt ) (7,266,686 annotations), [shuffle_labels.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthText/shuffle_labels.txt ) (2,400,000 randomly sampled annotations), [alphanumeric_labels.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthText/alphanumeric_labels.txt ) (7,239,272 annotations with alphanumeric characters only) and [instances_train.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthText/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.
:::
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- Step3:
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```bash
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 .
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mv /path/to/alphanumeric_labels.txt .
mv /path/to/instances_train.txt .
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# create soft link
cd /path/to/mmocr/data/mixture
ln -s /path/to/SynthText SynthText
```
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- Step4:
Generate cropped images and labels:
```bash
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 ](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg ) (code:627x))
- Step2: Download [label.txt ](https://download.openmmlab.com/mmocr/data/mixture/SynthAdd/label.txt )
- Step3:
```bash
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
```
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:::{tip}
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To convert label file with `txt` format to `lmdb` format,
```bash
python tools/data/utils/txt2lmdb.py -i < txt_label_path > -o < lmdb_label_path >
```
For example,
```bash
python tools/data/utils/txt2lmdb.py -i data/mixture/Syn90k/label.txt -o data/mixture/Syn90k/label.lmdb
```
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:::
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### 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_label.txt` , `val_label.txt` and crop images using 4 processes with the following command:
```bash
python tools/data/textrecog/textocr_converter.py /path/to/textocr 4
```
### Totaltext
- Step1: Download `totaltext.zip` from [github dataset ](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset ) and `groundtruth_text.zip` 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 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/` ):
```bash
python tools/data/textrecog/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
```
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### OpenVINO
- Step0: Install [awscli ](https://aws.amazon.com/cli/ ).
- Step1: 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
```
- Step2: Generate `train_{1,2,5,f}_label.txt` , `val_label.txt` and crop images using 4 processes with the following command:
```bash
python tools/data/textrecog/openvino_converter.py /path/to/openvino 4
```
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### FUNSD
- 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_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):
```bash
python tools/data/textrecog/funsd_converter.py PATH/TO/funsd --nproc 4
```
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### SROIE
- Step1: Step1: Download `0325updated.task1train(626p).zip` , `task1&2_test(361p).zip` , and `text.task1&2-test（ 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（
# 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-testг
rm 0325updated.task1train\(626p\).zip && rm task1\&2_test\(361p\).zip && rm text.task1\&2-test（
```
- Step3: Generate `train_label.jsonl` and `test_label.jsonl` and crop images using 4 processes with the following command:
```bash
python tools/data/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_label.jsonl
│ ├── test_label.jsonl
```
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### Lecture Video DB
**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_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:
```bash
python tools/data/textdreog/lv_converter.py PATH/TO/lv
```
