diff --git a/docs/en/user_guides/data_prepare/dataset_preparer.md b/docs/en/user_guides/data_prepare/dataset_preparer.md
index 51a8a7df..65ccf5ab 100644
--- a/docs/en/user_guides/data_prepare/dataset_preparer.md
+++ b/docs/en/user_guides/data_prepare/dataset_preparer.md
@@ -33,7 +33,7 @@ Also, the script supports preparing multiple datasets at the same time. For exam
python tools/dataset_converters/prepare_dataset.py icdar2015 totaltext --task textrecog
```
-To check the supported datasets in MMOCR, please refer to [Dataset Zoo](./datasetzoo.md).
+To check the supported datasets of Dataset Preparer, please refer to [Dataset Zoo](./datasetzoo.md). Some of other datasets that need to be prepared manually are listed in [Text Detection](./det.md) and [Text Recognition](./recog.md).
## Advanced Usage
diff --git a/docs/en/user_guides/data_prepare/det.md b/docs/en/user_guides/data_prepare/det.md
index 8c20fb21..c9ec95b4 100644
--- a/docs/en/user_guides/data_prepare/det.md
+++ b/docs/en/user_guides/data_prepare/det.md
@@ -1,40 +1,32 @@
# Text Detection
```{note}
-This page is deprecated and all these scripts will be eventually migrated into dataset preparer, a brand new module designed to ease these lengthy dataset preparation steps. [Check it out](./dataset_preparer.md)!
+This page is a manual preparation guide for datasets not yet supported by [Dataset Preparer](./dataset_preparer.md), which all these scripts will be eventually migrated into.
```
## Overview
-| Dataset | Images | | Annotation Files | | |
-| :---------------: | :-------------------------------------------: | :-------------------------------------: | :------------------------------------------------------: | :--------------------------------------: | :-: |
-| | | training | validation | testing | |
-| CTW1500 | [homepage](https://github.com/Yuliang-Liu/Curve-Text-Detector) | - | - | - | |
-| ICDAR2011 | [homepage](https://rrc.cvc.uab.es/?ch=1) | - | - | | |
-| ICDAR2013 | [homepage](https://rrc.cvc.uab.es/?ch=2) | - | - | - | |
-| 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/) | - | - | - | |
-| DeText | [homepage](https://rrc.cvc.uab.es/?ch=9) | - | - | - | |
-| NAF | [homepage](https://github.com/herobd/NAF_dataset/releases/tag/v1.0) | - | - | - | |
-| SROIE | [homepage](https://rrc.cvc.uab.es/?ch=13) | - | - | - | |
-| Lecture Video DB | [homepage](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb) | - | - | - | |
-| LSVT | [homepage](https://rrc.cvc.uab.es/?ch=16) | - | - | - | |
-| IMGUR | [homepage](https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset) | - | - | - | |
-| KAIST | [homepage](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database) | - | - | - | |
-| MTWI | [homepage](https://tianchi.aliyun.com/competition/entrance/231685/information?lang=en-us) | - | - | - | |
-| COCO Text v2 | [homepage](https://bgshih.github.io/cocotext/) | - | - | - | |
-| ReCTS | [homepage](https://rrc.cvc.uab.es/?ch=12) | - | - | - | |
-| IIIT-ILST | [homepage](http://cvit.iiit.ac.in/research/projects/cvit-projects/iiit-ilst) | - | - | - | |
-| VinText | [homepage](https://github.com/VinAIResearch/dict-guided) | - | - | - | |
-| BID | [homepage](https://github.com/ricardobnjunior/Brazilian-Identity-Document-Dataset) | - | - | - | |
-| RCTW | [homepage](https://rctw.vlrlab.net/index.html) | - | - | - | |
-| HierText | [homepage](https://github.com/google-research-datasets/hiertext) | - | - | - | |
-| ArT | [homepage](https://rrc.cvc.uab.es/?ch=14) | - | - | - | |
+| Dataset | Images | | Annotation Files | | |
+| :---------------: | :------------------------------------------------------: | :------------------------------------------------: | :-----------------------------------------------------------------: | :-----: | :-: |
+| | | training | validation | testing | |
+| CTW1500 | [homepage](https://github.com/Yuliang-Liu/Curve-Text-Detector) | - | - | - | |
+| ICDAR2011 | [homepage](https://rrc.cvc.uab.es/?ch=1) | - | - | | |
+| 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)) | - | - | |
+| 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) | - | - | |
+| DeText | [homepage](https://rrc.cvc.uab.es/?ch=9) | - | - | - | |
+| Lecture Video DB | [homepage](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb) | - | - | - | |
+| LSVT | [homepage](https://rrc.cvc.uab.es/?ch=16) | - | - | - | |
+| IMGUR | [homepage](https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset) | - | - | - | |
+| KAIST | [homepage](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database) | - | - | - | |
+| MTWI | [homepage](https://tianchi.aliyun.com/competition/entrance/231685/information?lang=en-us) | - | - | - | |
+| ReCTS | [homepage](https://rrc.cvc.uab.es/?ch=12) | - | - | - | |
+| IIIT-ILST | [homepage](http://cvit.iiit.ac.in/research/projects/cvit-projects/iiit-ilst) | - | - | - | |
+| VinText | [homepage](https://github.com/VinAIResearch/dict-guided) | - | - | - | |
+| BID | [homepage](https://github.com/ricardobnjunior/Brazilian-Identity-Document-Dataset) | - | - | - | |
+| RCTW | [homepage](https://rctw.vlrlab.net/index.html) | - | - | - | |
+| HierText | [homepage](https://github.com/google-research-datasets/hiertext) | - | - | - | |
+| ArT | [homepage](https://rrc.cvc.uab.es/?ch=14) | - | - | - | |
### Install AWS CLI (optional)
@@ -142,82 +134,6 @@ inconsistency results in false examples in the training set. Therefore, users sh
│ └── instances_training.json
```
-## ICDAR 2013 (Focused Scene Text)
-
-- Step1: Download `Challenge2_Training_Task12_Images.zip`, `Challenge2_Test_Task12_Images.zip`, `Challenge2_Training_Task1_GT.zip`, and `Challenge2_Test_Task1_GT.zip` from [homepage](https://rrc.cvc.uab.es/?ch=2&com=downloads) `Task 2.1: Text Localization (2013 edition)`.
-
- ```bash
- mkdir icdar2013 && cd icdar2013
- mkdir imgs && mkdir annotations
-
- # Download ICDAR 2013
- wget https://rrc.cvc.uab.es/downloads/Challenge2_Training_Task12_Images.zip --no-check-certificate
- wget https://rrc.cvc.uab.es/downloads/Challenge2_Test_Task12_Images.zip --no-check-certificate
- wget https://rrc.cvc.uab.es/downloads/Challenge2_Training_Task1_GT.zip --no-check-certificate
- wget https://rrc.cvc.uab.es/downloads/Challenge2_Test_Task1_GT.zip --no-check-certificate
-
- # For images
- unzip -q Challenge2_Training_Task12_Images.zip -d imgs/training
- unzip -q Challenge2_Test_Task12_Images.zip -d imgs/test
- # For annotations
- unzip -q Challenge2_Training_Task1_GT.zip -d annotations/training
- unzip -q Challenge2_Test_Task1_GT.zip -d annotations/test
-
- rm Challenge2_Training_Task12_Images.zip && rm Challenge2_Test_Task12_Images.zip && rm Challenge2_Training_Task1_GT.zip && rm Challenge2_Test_Task1_GT.zip
- ```
-
-- Step 2: Generate `instances_training.json` and `instances_test.json` with the following command:
-
- ```bash
- python tools/dataset_converters/textdet/ic13_converter.py PATH/TO/icdar2013 --nproc 4
- ```
-
-- After running the above codes, the directory structure should be as follows:
-
- ```text
- │── icdar2013
- │ ├── imgs
- │ ├── instances_test.json
- │ └── instances_training.json
- ```
-
-## 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 the following command:
-
- ```bash
- python tools/dataset_converters/textdet/icdar_converter.py /path/to/icdar2015 -o /path/to/icdar2015 -d icdar2015 --split-list training test
- ```
-
-- The resulting directory structure looks like the following:
-
- ```text
- ├── icdar2015
- │ ├── imgs
- │ ├── annotations
- │ ├── instances_test.json
- │ └── instances_training.json
- ```
-
## ICDAR 2017
- Follow similar steps as [ICDAR 2015](#icdar-2015).
@@ -248,81 +164,6 @@ inconsistency results in false examples in the training set. Therefore, users sh
│ └── lock.mdb
```
-## 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/dataset_converters/textdet/textocr_converter.py /path/to/textocr
- ```
-
-- The resulting directory structure looks like the following:
-
- ```text
- ├── textocr
- │ ├── train
- │ ├── instances_training.json
- │ └── instances_val.json
- ```
-
-## 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` or `TT_new_train_GT.zip` (if you prefer to use the latest version of training annotations) 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 legacy training and test annotations
- unzip groundtruth_text.zip
- mv Groundtruth/Polygon/Train annotations/training
- mv Groundtruth/Polygon/Test annotations/test
-
- # Using the latest training annotations
- # WARNING: Delete legacy train annotations before running the following command.
- unzip TT_new_train_GT.zip
- mv Train annotations/training
- ```
-
-- Step2: Generate `instances_training.json` and `instances_test.json` with the following command:
-
- ```bash
- python tools/dataset_converters/textdet/totaltext_converter.py /path/to/totaltext
- ```
-
-- The resulting directory structure looks like the following:
-
- ```text
- ├── totaltext
- │ ├── imgs
- │ ├── annotations
- │ ├── instances_test.json
- │ └── instances_training.json
- ```
-
## 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/`.
@@ -358,43 +199,6 @@ inconsistency results in false examples in the training set. Therefore, users sh
│ └── instances_training.json
```
-## 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/dataset_converters/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
- ```
-
-- The resulting directory structure looks like the following:
-
- ```text
- │── funsd
- │ ├── annotations
- │ ├── imgs
- │ ├── instances_test.json
- │ └── instances_training.json
- ```
-
## DeText
- Step1: Download `ch9_training_images.zip`, `ch9_training_localization_transcription_gt.zip`, `ch9_validation_images.zip`, and `ch9_validation_localization_transcription_gt.zip` from **Task 3: End to End** on the [homepage](https://rrc.cvc.uab.es/?ch=9).
@@ -432,84 +236,6 @@ inconsistency results in false examples in the training set. Therefore, users sh
│ └── instances_training.json
```
-## NAF
-
-- Step1: Download [labeled_images.tar.gz](https://github.com/herobd/NAF_dataset/releases/tag/v1.0) to `naf/`.
-
- ```bash
- mkdir naf && cd naf
-
- # Download NAF dataset
- wget https://github.com/herobd/NAF_dataset/releases/download/v1.0/labeled_images.tar.gz
- tar -zxf labeled_images.tar.gz
-
- # For images
- mkdir annotations && mv labeled_images imgs
-
- # For annotations
- git clone https://github.com/herobd/NAF_dataset.git
- mv NAF_dataset/train_valid_test_split.json annotations/ && mv NAF_dataset/groups annotations/
-
- rm -rf NAF_dataset && rm labeled_images.tar.gz
- ```
-
-- Step2: Generate `instances_training.json`, `instances_val.json`, and `instances_test.json` with following command:
-
- ```bash
- python tools/dataset_converters/textdet/naf_converter.py PATH/TO/naf --nproc 4
- ```
-
-- After running the above codes, the directory structure should be as follows:
-
- ```text
- │── naf
- │ ├── annotations
- │ ├── imgs
- │ ├── instances_test.json
- │ ├── instances_val.json
- │ └── instances_training.json
- ```
-
-## SROIE
-
-- Step1: Download `0325updated.task1train(626p).zip`, `task1&2_test(361p).zip`, and `text.task1&2-test（361p).zip` 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（361p\).zip
-
- # 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-testги361p\)/ annotations/test
-
- rm 0325updated.task1train\(626p\).zip && rm task1\&2_test\(361p\).zip && rm text.task1\&2-test（361p\).zip
- ```
-
-- Step3: Generate `instances_training.json` and `instances_test.json` with the following command:
-
- ```bash
- python tools/dataset_converters/textdet/sroie_converter.py PATH/TO/sroie --nproc 4
- ```
-
-- After running the above codes, the directory structure should be as follows:
-
- ```text
- ├── sroie
- │ ├── annotations
- │ ├── imgs
- │ ├── instances_test.json
- │ └── instances_training.json
- ```
-
## Lecture Video DB
- Step1: Download [IIIT-CVid.zip](http://cdn.iiit.ac.in/cdn/preon.iiit.ac.in/~kartik/IIIT-CVid.zip) to `lv/`.
@@ -684,40 +410,6 @@ inconsistency results in false examples in the training set. Therefore, users sh
│ └── instances_val.json (optional)
```
-## COCO Text v2
-
-- Step1: Download image [train2014.zip](http://images.cocodataset.org/zips/train2014.zip) and annotation [cocotext.v2.zip](https://github.com/bgshih/cocotext/releases/download/dl/cocotext.v2.zip) to `coco_textv2/`.
-
- ```bash
- mkdir coco_textv2 && cd coco_textv2
- mkdir annotations
-
- # Download COCO Text v2 dataset
- wget http://images.cocodataset.org/zips/train2014.zip
- wget https://github.com/bgshih/cocotext/releases/download/dl/cocotext.v2.zip
- unzip -q train2014.zip && unzip -q cocotext.v2.zip
-
- mv train2014 imgs && mv cocotext.v2.json annotations/
-
- rm train2014.zip && rm -rf cocotext.v2.zip
- ```
-
-- Step2: Generate `instances_training.json` and `instances_val.json` with the following command:
-
- ```bash
- python tools/dataset_converters/textdet/cocotext_converter.py PATH/TO/coco_textv2
- ```
-
-- After running the above codes, the directory structure should be as follows:
-
- ```text
- │── coco_textv2
- │ ├── annotations
- │ ├── imgs
- │ ├── instances_training.json
- │ └── instances_val.json
- ```
-
## ReCTS
- Step1: Download [ReCTS.zip](https://datasets.cvc.uab.es/rrc/ReCTS.zip) to `rects/` from the [homepage](https://rrc.cvc.uab.es/?ch=12&com=downloads).
diff --git a/docs/en/user_guides/data_prepare/kie.md b/docs/en/user_guides/data_prepare/kie.md
index d10679ed..9d324383 100644
--- a/docs/en/user_guides/data_prepare/kie.md
+++ b/docs/en/user_guides/data_prepare/kie.md
@@ -1,7 +1,7 @@
# Key Information Extraction
```{note}
-This page is deprecated and all these scripts will be eventually migrated into dataset preparer, a brand new module designed to ease these lengthy dataset preparation steps. [Check it out](./dataset_preparer.md)!
+This page is a manual preparation guide for datasets not yet supported by [Dataset Preparer](./dataset_preparer.md), which all these scripts will be eventually migrated into.
```
## Overview
diff --git a/docs/en/user_guides/data_prepare/recog.md b/docs/en/user_guides/data_prepare/recog.md
index 3fdc8ab0..3efa863d 100644
--- a/docs/en/user_guides/data_prepare/recog.md
+++ b/docs/en/user_guides/data_prepare/recog.md
@@ -1,7 +1,7 @@
# Text Recognition
```{note}
-This page is deprecated and all these scripts will be eventually migrated into dataset preparer, a brand new module designed to ease these lengthy dataset preparation steps. [Check it out](./dataset_preparer.md)!
+This page is a manual preparation guide for datasets not yet supported by [Dataset Preparer](./dataset_preparer.md), which all these scripts will be eventually migrated into.
```
## Overview
@@ -11,28 +11,16 @@ This page is deprecated and all these scripts will be eventually migrated into d
| | | training | test |
| coco_text | [homepage](https://rrc.cvc.uab.es/?ch=5&com=downloads) | [train_labels.json](#TODO) | - |
| ICDAR2011 | [homepage](https://rrc.cvc.uab.es/?ch=1) | - | - |
-| ICDAR2013 | [homepage](https://rrc.cvc.uab.es/?ch=2) | - | - |
-| icdar_2015 | [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads) | [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/icdar_2015/train_labels.json) | [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/icdar_2015/test_labels.json) |
-| IIIT5K | [homepage](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html) | [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/IIIT5K/train_labels.json) | [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/IIIT5K/test_labels.json) |
-| ct80 | [homepage](http://cs-chan.com/downloads_CUTE80_dataset.html) | - | [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/ct80/test_labels.json) |
-| svt | [homepage](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset) | - | [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/svt/test_labels.json) |
-| svtp | [unofficial homepage\[1\]](https://github.com/Jyouhou/Case-Sensitive-Scene-Text-Recognition-Datasets) | - | [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/svtp/test_labels.json) |
| MJSynth (Syn90k) | [homepage](https://www.robots.ox.ac.uk/~vgg/data/text/) | [subset_train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/Syn90k/subset_train_labels.json) \| [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/Syn90k/train_labels.json) | - |
| SynthText (Synth800k) | [homepage](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) | [alphanumeric_train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/SynthText/alphanumeric_train_labels.json) \|[subset_train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/SynthText/subset_train_labels.json) \| [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/SynthText/train_labels.json) | - |
| SynthAdd | [SynthText_Add.zip](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg) (code:627x) | [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/synthtext_add/train_labels.json) | - |
-| 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/) | - | - |
| DeText | [homepage](https://rrc.cvc.uab.es/?ch=9) | - | - |
-| NAF | [homepage](https://github.com/herobd/NAF_dataset) | - | - |
-| SROIE | [homepage](https://rrc.cvc.uab.es/?ch=13) | - | - |
| Lecture Video DB | [homepage](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb) | - | - |
| LSVT | [homepage](https://rrc.cvc.uab.es/?ch=16) | - | - |
| IMGUR | [homepage](https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset) | - | - |
| KAIST | [homepage](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database) | - | - |
| MTWI | [homepage](https://tianchi.aliyun.com/competition/entrance/231685/information?lang=en-us) | - | - |
-| COCO Text v2 | [homepage](https://bgshih.github.io/cocotext/) | - | - |
| ReCTS | [homepage](https://rrc.cvc.uab.es/?ch=12) | - | - |
| IIIT-ILST | [homepage](http://cvit.iiit.ac.in/research/projects/cvit-projects/iiit-ilst) | - | - |
| VinText | [homepage](https://github.com/VinAIResearch/dict-guided) | - | - |
@@ -98,141 +86,6 @@ This page is deprecated and all these scripts will be eventually migrated into d
│ └── test_labels.json
```
-## ICDAR 2013 (Focused Scene Text)
-
-- Step1: Download `Challenge2_Training_Task3_Images_GT.zip`, `Challenge2_Test_Task3_Images.zip`, and `Challenge2_Test_Task3_GT.txt` from [homepage](https://rrc.cvc.uab.es/?ch=2&com=downloads) `Task 2.3: Word Recognition (2013 edition)`.
-
- ```bash
- mkdir icdar2013 && cd icdar2013
- mkdir annotations
-
- # Download ICDAR 2013
- wget https://rrc.cvc.uab.es/downloads/Challenge2_Training_Task3_Images_GT.zip --no-check-certificate
- wget https://rrc.cvc.uab.es/downloads/Challenge2_Test_Task3_Images.zip --no-check-certificate
- wget https://rrc.cvc.uab.es/downloads/Challenge2_Test_Task3_GT.txt --no-check-certificate
- wget https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/test_label_1015.txt
-
- # For images
- mkdir crops
- unzip -q Challenge2_Training_Task3_Images_GT.zip -d crops/train
- unzip -q Challenge2_Test_Task3_Images.zip -d crops/test
- # For annotations
- mv Challenge2_Test_Task3_GT.txt annotations && mv crops/train/gt.txt annotations/Challenge2_Train_Task3_GT.txt && mv test_label_1015.txt annotations/Challenge2_Test1015_Task3_GT.txt
-
- rm Challenge2_Training_Task3_Images_GT.zip && rm Challenge2_Test_Task3_Images.zip
- ```
-
-- Step 2: Generate `train_labels.json`, `test_labels.json`, `test1015_label.json` with the following command:
-
- ```bash
- python tools/dataset_converters/textrecog/ic13_converter.py PATH/TO/icdar2013
- ```
-
-- After running the above codes, the directory structure should be as follows:
-
- ```text
- ├── icdar2013
- │ ├── crops
- │ ├── train_labels.json
- │ ├── test_labels.json
- │ └── test1015_label.json
- ```
-
-## 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_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/icdar_2015/train_labels.json) and [test_label.json](https://download.openmmlab.com/mmocr/data/1.x/recog/icdar_2015/test_labels.json)
-
-- After running the above codes, the directory structure
- should be as follows:
-
- ```text
- ├── icdar_2015
- │ ├── train_labels.json
- │ ├── test_labels.json
- │ ├── ch4_training_word_images_gt
- │ └── ch4_test_word_images_gt
- ```
-
-## IIIT5K
-
-- Step1: Download `IIIT5K-Word_V3.0.tar.gz` from [homepage](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html)
-
-- Step2: Download [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/IIIT5K/train_labels.json) and [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/IIIT5K/test_labels.json)
-
-- After running the above codes, the directory structure
- should be as follows:
-
- ```text
- ├── III5K
- │ ├── train_labels.json
- │ ├── test_labels.json
- │ ├── train
- │ └── test
- ```
-
-## svt
-
-- Step1: Download `svt.zip` form [homepage](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset)
-
-- Step2: Download [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/svt/test_labels.json)
-
-- Step3:
-
- ```bash
- python tools/dataset_converters/textrecog/svt_converter.py <download_svt_dir_path>
- ```
-
-- After running the above codes, the directory structure
- should be as follows:
-
- ```text
- ├── svt
- │ ├── test_labels.json
- │ └── image
- ```
-
-## ct80
-
-- Step1: Download [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/ct80/test_labels.json)
-
-- Step2: Download [timage.tar.gz](https://download.openmmlab.com/mmocr/data/mixture/ct80/timage.tar.gz)
-
-- Step3:
-
- ```bash
- mkdir ct80 && cd ct80
- mv /path/to/test_labels.json .
- mv /path/to/timage.tar.gz .
- tar -xvf timage.tar.gz
- # create soft link
- cd /path/to/mmocr/data/mixture
- ln -s /path/to/ct80 ct80
- ```
-
-- After running the above codes, the directory structure
- should be as follows:
-
- ```text
- ├── ct80
- │ ├── test_labels.json
- │ └── timage
- ```
-
-## svtp
-
-- Step1: Download [test_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/svtp/test_labels.json)
-
-- After running the above codes, the directory structure
- should be as follows:
-
- ```text
- ├── svtp
- │ ├── test_labels.json
- │ └── image
- ```
-
## coco_text
- Step1: Download from [homepage](https://rrc.cvc.uab.es/?ch=5&com=downloads)
@@ -365,79 +218,6 @@ Please make sure you're using the right annotation to train the model by checkin
│ └── SynthText_Add
```
-## 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_labels.json`, `val_labels.json` and crop images using 4 processes with the following command:
-
- ```bash
- python tools/dataset_converters/textrecog/textocr_converter.py /path/to/textocr 4
- ```
-
-- After running the above codes, the directory structure
- should be as follows:
-
- ```text
- ├── TextOCR
- │ ├── image
- │ ├── train_labels.json
- │ └── val_labels.json
- ```
-
-## Totaltext
-
-- Step1: Download `totaltext.zip` from [github dataset](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset) and `groundtruth_text.zip` or `TT_new_train_GT.zip` (if you prefer to use the latest version of training annotations) 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 legacy training and test annotations
- unzip groundtruth_text.zip
- mv Groundtruth/Polygon/Train annotations/training
- mv Groundtruth/Polygon/Test annotations/test
-
- # Using the latest training annotations
- # WARNING: Delete legacy train annotations before running the following command.
- unzip TT_new_train_GT.zip
- mv Train annotations/training
- ```
-
-- Step2: Generate cropped images, `train_labels.json` and `test_labels.json` with the following command (the cropped images will be saved to `data/totaltext/dst_imgs/`):
-
- ```bash
- python tools/dataset_converters/textrecog/totaltext_converter.py /path/to/totaltext
- ```
-
-- After running the above codes, the directory structure should be as follows:
-
- ```text
- ├── totaltext
- │ ├── dst_imgs
- │ ├── train_labels.json
- │ └── test_labels.json
- ```
-
## OpenVINO
- Step1 (optional): Install [AWS CLI](https://mmocr.readthedocs.io/en/latest/datasets/recog.html#install-aws-cli-optional).
@@ -569,45 +349,6 @@ Please make sure you're using the right annotation to train the model by checkin
│ └── test_labels.json
```
-## SROIE
-
-- Step1: Step1: Download `0325updated.task1train(626p).zip`, `task1&2_test(361p).zip`, and `text.task1&2-test（361p).zip` 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（361p\).zip
-
- # 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-testги361p\)/ annotations/test
-
- rm 0325updated.task1train\(626p\).zip && rm task1\&2_test\(361p\).zip && rm text.task1\&2-test（361p\).zip
- ```
-
-- Step3: Generate `train_labels.json` and `test_labels.json` and crop images using 4 processes with the following command:
-
- ```bash
- python tools/dataset_converters/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_labels.json
- │ └── test_labels.json
- ```
-
## Lecture Video DB
```{warning}
@@ -695,49 +436,6 @@ This section is not fully tested yet.
│ └── val_label.json (optional)
```
-## FUNSD
-
-```{warning}
-This section is not fully tested yet.
-```
-
-- 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_labels.json` and `test_labels.json` 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/dataset_converters/textrecog/funsd_converter.py PATH/TO/funsd --nproc 4
- ```
-
-- After running the above codes, the directory structure
- should be as follows:
-
- ```text
- ├── funsd
- │ ├── imgs
- │ ├── crops
- │ ├── annotations
- │ ├── train_labels.json
- │ └── test_labels.json
- ```
-
## IMGUR
```{warning}
@@ -855,46 +553,6 @@ This section is not fully tested yet.
│ └── val_label.json (optional)
```
-## COCO Text v2
-
-```{warning}
-This section is not fully tested yet.
-```
-
-- Step1: Download image [train2014.zip](http://images.cocodataset.org/zips/train2014.zip) and annotation [cocotext.v2.zip](https://github.com/bgshih/cocotext/releases/download/dl/cocotext.v2.zip) to `coco_textv2/`.
-
- ```bash
- mkdir coco_textv2 && cd coco_textv2
- mkdir annotations
-
- # Download COCO Text v2 dataset
- wget http://images.cocodataset.org/zips/train2014.zip
- wget https://github.com/bgshih/cocotext/releases/download/dl/cocotext.v2.zip
- unzip -q train2014.zip && unzip -q cocotext.v2.zip
-
- mv train2014 imgs && mv cocotext.v2.json annotations/
-
- rm train2014.zip && rm -rf cocotext.v2.zip
- ```
-
-- Step2: Generate `train_labels.json` and `val_label.json` with the following command:
-
- ```bash
- # Add --preserve-vertical to preserve vertical texts for training, otherwise
- # vertical images will be filtered and stored in PATH/TO/mtwi/ignores
- python tools/dataset_converters/textrecog/cocotext_converter.py PATH/TO/coco_textv2 --nproc 4
- ```
-
-- After running the above codes, the directory structure should be as follows:
-
- ```text
- ├── coco_textv2
- │ ├── crops
- │ ├── ignores
- │ ├── train_labels.json
- │ └── val_label.json
- ```
-
## ReCTS
```{warning}
diff --git a/docs/zh_cn/user_guides/data_prepare/dataset_preparer.md b/docs/zh_cn/user_guides/data_prepare/dataset_preparer.md
index 0af4bb17..e57cdf44 100644
--- a/docs/zh_cn/user_guides/data_prepare/dataset_preparer.md
+++ b/docs/zh_cn/user_guides/data_prepare/dataset_preparer.md
@@ -34,7 +34,7 @@ python tools/dataset_converters/prepare_dataset.py icdar2015 --task textdet --ov
python tools/dataset_converters/prepare_dataset.py icdar2015 totaltext --task textrecog --overwrite-cfg
```
-进一步了解 MMOCR 支持的数据集，您可以浏览[支持的数据集文档](./datasetzoo.md)
+进一步了解 Dataset Preparer 支持的数据集，您可以浏览[支持的数据集文档](./datasetzoo.md)。一些需要手动准备的数据集也列在了 [文字检测](./det.md) 和 [文字识别](./recog.md) 内。
## 进阶用法
diff --git a/docs/zh_cn/user_guides/data_prepare/det.md b/docs/zh_cn/user_guides/data_prepare/det.md
index 960122a9..a2b0aa40 100644
--- a/docs/zh_cn/user_guides/data_prepare/det.md
+++ b/docs/zh_cn/user_guides/data_prepare/det.md
@@ -1,42 +1,15 @@
# 文字检测
+```{warning}
+该页面版本落后于英文版文档，请切换至英文阅读最新文档。
+```
+
```{note}
-该页面内容已经过时，所有有关数据格式转换相关的脚本都将最终迁移至数据准备器 **dataset preparer**，这个全新设计的模块能够极大地方便用户完成冗长的数据准备步骤，详见[相关文档](./dataset_preparer.md)。
+我们正努力往 [Dataset Preparer](./dataset_preparer.md) 中增加更多数据集。对于 [Dataset Preparer](./dataset_preparer.md) 暂未能完整支持的数据集，本页提供了一系列手动下载的步骤，供有需要的用户使用。
```
## 概览
-文字检测任务的数据集应按如下目录配置：
-
-```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
-```
-
| 数据集名称 | 数据图片 | | 标注文件 | |
| :--------: | :-----------------------------------------------: | :-------------------------------------------: | :------------------------------------------------: | :--------------------------------------------: |
| | | 训练集 (training) | 验证集 (validation) | 测试集 (testing) |
diff --git a/docs/zh_cn/user_guides/data_prepare/kie.md b/docs/zh_cn/user_guides/data_prepare/kie.md
index 6a707287..ce8d1462 100644
--- a/docs/zh_cn/user_guides/data_prepare/kie.md
+++ b/docs/zh_cn/user_guides/data_prepare/kie.md
@@ -1,7 +1,7 @@
# 关键信息提取
```{note}
-该页面内容已经过时，所有有关数据格式转换相关的脚本都将最终迁移至数据准备器 **dataset preparer**，这个全新设计的模块能够极大地方便用户完成冗长的数据准备步骤，详见[相关文档](./dataset_preparer.md)。
+我们正努力往 [Dataset Preparer](./dataset_preparer.md) 中增加更多数据集。对于 [Dataset Preparer](./dataset_preparer.md) 暂未能完整支持的数据集，本页提供了一系列手动下载的步骤，供有需要的用户使用。
```
## 概览
diff --git a/docs/zh_cn/user_guides/data_prepare/recog.md b/docs/zh_cn/user_guides/data_prepare/recog.md
index f10a0da8..184426b4 100644
--- a/docs/zh_cn/user_guides/data_prepare/recog.md
+++ b/docs/zh_cn/user_guides/data_prepare/recog.md
@@ -1,7 +1,11 @@
# 文字识别
+```{warning}
+该页面版本落后于英文版文档，请切换至英文阅读最新文档。
+```
+
```{note}
-该页面内容已经过时，所有有关数据格式转换相关的脚本都将最终迁移至数据准备器 **dataset preparer**，这个全新设计的模块能够极大地方便用户完成冗长的数据准备步骤，详见[相关文档](./dataset_preparer.md)。
+我们正努力往 [Dataset Preparer](./dataset_preparer.md) 中增加更多数据集。对于 [Dataset Preparer](./dataset_preparer.md) 暂未能完整支持的数据集，本页提供了一系列手动下载的步骤，供有需要的用户使用。
```
## 概览