[Fix] Clear up some unused scripts (#1798)

Author: gaotongxiao

docs/en/user_guides/data_prepare/det.md

@@ -9,10 +9,8 @@ This page is a manual preparation guide for datasets not yet supported by [Datas
 |      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) |                         -                          |                                  -                                  |    -    |     |
@@ -62,47 +60,6 @@ backend used in MMCV would read them and apply the rotation on the images.  Howe
 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)
 ```
 
-## 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/dataset_converters/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1500 --split-list training test
-  ```
-
-- The resulting directory structure looks like the following:
-
-  ```text
-  ├── ctw1500
-  │   ├── imgs
-  │   ├── annotations
-  │   ├── instances_training.json
-  │   └── instances_val.json
-  ```
-
 ## ICDAR 2011 (Born-Digital Images)
 
 - Step1: Download `Challenge1_Training_Task12_Images.zip`, `Challenge1_Training_Task1_GT.zip`, `Challenge1_Test_Task12_Images.zip`, and `Challenge1_Test_Task1_GT.zip` from [homepage](https://rrc.cvc.uab.es/?ch=1&com=downloads) `Task 1.1: Text Localization (2013 edition)`.
@@ -156,22 +113,6 @@ inconsistency results in false examples in the training set. Therefore, users sh
   │   └── instances_val.json
   ```
 
-## SynthText
-
-- Step1: Download SynthText.zip from \[homepage\](<https://www.robots.ox.ac.uk/~vgg/data/scenetext/> and extract its content to `synthtext/img`.
-
-- Step2: 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/`.
-
-- The resulting directory structure looks like the following:
-
-  ```text
-  ├── synthtext
-  │   ├── imgs
-  │   └── instances_training.lmdb
-  │       ├── data.mdb
-  │       └── lock.mdb
-  ```
-
 ## 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/`.

docs/en/user_guides/data_prepare/recog.md

@@ -11,7 +11,6 @@ This page is a manual preparation guide for datasets not yet supported by [Datas
 |                  |                                                         |                             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)         |                                -                                 |                                 -                                 |
-| 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) |                                 -                                 |
 |     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) |                                 -                                 |
 |     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) |
 |      DeText      |        [homepage](https://rrc.cvc.uab.es/?ch=9)         |                                -                                 |                                 -                                 |
@@ -110,44 +109,6 @@ For users in China, these datasets can also be downloaded from [OpenDataLab](htt
   │   └── train_words
   ```
 
-## MJSynth (Syn90k)
-
-- Step1: Download `mjsynth.tar.gz` from [homepage](https://www.robots.ox.ac.uk/~vgg/data/text/)
-- Step2: Download [train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/Syn90k/train_labels.json) (8,919,273 annotations) and [subset_train_labels.json](https://download.openmmlab.com/mmocr/data/1.x/recog/Syn90k/subset_train_labels.json) (2,400,000 randomly sampled annotations).
-
-```{note}
-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/subset_train_labels.json .
-  mv /path/to/train_labels.json .
-
-  # create soft link
-  cd /path/to/mmocr/data/recog/
-
-  ln -s /path/to/Syn90k Syn90k
-
-  ```
-
-- After running the above codes, the directory structure
-  should be as follows:
-
-  ```text
-  ├── Syn90k
-  │   ├── subset_train_labels.json
-  │   ├── train_labels.json
-  │   └── mnt
-  ```
-
 ## SynthAdd
 
 - Step1: Download `SynthText_Add.zip` from [SynthAdd](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg) (code:627x))