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| # 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 <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/) | |
| - 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 <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 | |
| ``` | |
| ::: | |
| ### 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 | |
| ``` | |