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| # Text Detection | |
| ## Overview | |
| The structure of the text detection dataset directory is organized as follows. | |
| ```text | |
| ├── ctw1500 | |
| │ ├── annotations | |
| │ ├── imgs | |
| │ ├── instances_test.json | |
| │ └── instances_training.json | |
| ├── icdar2015 | |
| │ ├── imgs | |
| │ ├── instances_test.json | |
| │ └── instances_training.json | |
| ├── icdar2017 | |
| │ ├── imgs | |
| │ ├── instances_training.json | |
| │ └── instances_val.json | |
| ├── synthtext | |
| │ ├── imgs | |
| │ └── instances_training.lmdb | |
| │ ├── data.mdb | |
| │ └── lock.mdb | |
| ├── textocr | |
| │ ├── train | |
| │ ├── instances_training.json | |
| │ └── instances_val.json | |
| ├── totaltext | |
| │ ├── imgs | |
| │ ├── instances_test.json | |
| │ └── instances_training.json | |
| ├── CurvedSynText150k | |
| │ ├── syntext_word_eng | |
| │ ├── emcs_imgs | |
| │ └── instances_training.json | |
| |── funsd | |
| | ├── annotations | |
| │ ├── imgs | |
| │ ├── instances_test.json | |
| │ └── instances_training.json | |
| ``` | |
| | Dataset | Images | | Annotation Files | | | | |
| | :---------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :---: | | |
| | | | training | validation | testing | | | |
| | CTW1500 | [homepage](https://github.com/Yuliang-Liu/Curve-Text-Detector) | - | - | - | | |
| | ICDAR2015 | [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads) | [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_training.json) | - | [instances_test.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_test.json) | | |
| | ICDAR2017 | [homepage](https://rrc.cvc.uab.es/?ch=8&com=downloads) | [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_training.json) | [instances_val.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_val.json) | - | | | | |
| | Synthtext | [homepage](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) | instances_training.lmdb ([data.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/data.mdb), [lock.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/lock.mdb)) | - | - | | |
| | TextOCR | [homepage](https://textvqa.org/textocr/dataset) | - | - | - | | |
| | Totaltext | [homepage](https://github.com/cs-chan/Total-Text-Dataset) | - | - | - | | |
| | CurvedSynText150k | [homepage](https://github.com/aim-uofa/AdelaiDet/blob/master/datasets/README.md) \| [Part1](https://drive.google.com/file/d/1OSJ-zId2h3t_-I7g_wUkrK-VqQy153Kj/view?usp=sharing) \| [Part2](https://drive.google.com/file/d/1EzkcOlIgEp5wmEubvHb7-J5EImHExYgY/view?usp=sharing) | [instances_training.json](https://download.openmmlab.com/mmocr/data/curvedsyntext/instances_training.json) | - | - | | |
| | FUNSD | [homepage](https://guillaumejaume.github.io/FUNSD/) | - | - | - | | |
| ## Important Note | |
| :::{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) | |
| ::: | |
| ## Preparation Steps | |
| ### ICDAR 2015 | |
| - Step0: Read [Important Note](#important-note) | |
| - Step1: Download `ch4_training_images.zip`, `ch4_test_images.zip`, `ch4_training_localization_transcription_gt.zip`, `Challenge4_Test_Task1_GT.zip` from [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads) | |
| - Step2: | |
| ```bash | |
| mkdir icdar2015 && cd icdar2015 | |
| mkdir imgs && mkdir annotations | |
| # For images, | |
| mv ch4_training_images imgs/training | |
| mv ch4_test_images imgs/test | |
| # For annotations, | |
| mv ch4_training_localization_transcription_gt annotations/training | |
| mv Challenge4_Test_Task1_GT annotations/test | |
| ``` | |
| - Step3: Download [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_training.json) and [instances_test.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_test.json) and move them to `icdar2015` | |
| - Or, generate `instances_training.json` and `instances_test.json` with following command: | |
| ```bash | |
| python tools/data/textdet/icdar_converter.py /path/to/icdar2015 -o /path/to/icdar2015 -d icdar2015 --split-list training test | |
| ``` | |
| ### ICDAR 2017 | |
| - Follow similar steps as [ICDAR 2015](#icdar-2015). | |
| ### CTW1500 | |
| - Step0: Read [Important Note](#important-note) | |
| - Step1: Download `train_images.zip`, `test_images.zip`, `train_labels.zip`, `test_labels.zip` from [github](https://github.com/Yuliang-Liu/Curve-Text-Detector) | |
| ```bash | |
| mkdir ctw1500 && cd ctw1500 | |
| mkdir imgs && mkdir annotations | |
| # For annotations | |
| cd annotations | |
| wget -O train_labels.zip https://universityofadelaide.box.com/shared/static/jikuazluzyj4lq6umzei7m2ppmt3afyw.zip | |
| wget -O test_labels.zip https://cloudstor.aarnet.edu.au/plus/s/uoeFl0pCN9BOCN5/download | |
| unzip train_labels.zip && mv ctw1500_train_labels training | |
| unzip test_labels.zip -d test | |
| cd .. | |
| # For images | |
| cd imgs | |
| wget -O train_images.zip https://universityofadelaide.box.com/shared/static/py5uwlfyyytbb2pxzq9czvu6fuqbjdh8.zip | |
| wget -O test_images.zip https://universityofadelaide.box.com/shared/static/t4w48ofnqkdw7jyc4t11nsukoeqk9c3d.zip | |
| unzip train_images.zip && mv train_images training | |
| unzip test_images.zip && mv test_images test | |
| ``` | |
| - Step2: Generate `instances_training.json` and `instances_test.json` with following command: | |
| ```bash | |
| python tools/data/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1500 --split-list training test | |
| ``` | |
| ### SynthText | |
| - Download [data.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/data.mdb) and [lock.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/lock.mdb) to `synthtext/instances_training.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 `instances_training.json` and `instances_val.json` with the following command: | |
| ```bash | |
| python tools/data/textdet/textocr_converter.py /path/to/textocr | |
| ``` | |
| ### Totaltext | |
| - Step0: Read [Important Note](#important-note) | |
| - 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 `instances_training.json` and `instances_test.json` with the following command: | |
| ```bash | |
| python tools/data/textdet/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test | |
| ``` | |
| ### CurvedSynText150k | |
| - Step1: Download [syntext1.zip](https://drive.google.com/file/d/1OSJ-zId2h3t_-I7g_wUkrK-VqQy153Kj/view?usp=sharing) and [syntext2.zip](https://drive.google.com/file/d/1EzkcOlIgEp5wmEubvHb7-J5EImHExYgY/view?usp=sharing) to `CurvedSynText150k/`. | |
| - Step2: | |
| ```bash | |
| 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](https://download.openmmlab.com/mmocr/data/curvedsyntext/instances_training.json) to `CurvedSynText150k/` | |
| - Or, generate `instances_training.json` with following command: | |
| ```bash | |
| python tools/data/common/curvedsyntext_converter.py PATH/TO/CurvedSynText150k --nproc 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 `instances_training.json` and `instances_test.json` with following command: | |
| ```bash | |
| python tools/data/textdet/funsd_converter.py PATH/TO/funsd --nproc 4 | |
| ``` | |