**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 config](https://github.com/open-mmlab/mmocr/blob/main/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py) for example)
- 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)
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/`.
- 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
