# Text Recognition ## 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 │ │ ├── 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](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/) | - | - | | (*) 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) ### ICDAR 2015 - 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 ``` ### 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/) - 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. ::: - Step3: ```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 . 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: ```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 ``` :::{tip} To convert label file with `txt` format to `lmdb` format, ```bash python tools/data/utils/txt2lmdb.py -i -o ``` For example, ```bash 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](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 ``` ### 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 ``` ### 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 ```