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# 文字识别
## 概览
**文字识别任务的数据集应按如下目录配置：**
```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
```
| 数据集名称 | 数据图片 | 标注文件 | 标注文件 |
| :--------: | :-----------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: |
| | | 训练集(training) | 测试集(test) |
| coco_text | [下载地址](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 | [下载地址](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 | [下载地址](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 | [下载地址](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 | [下载地址](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 | [下载地址](http://cs-chan.com/downloads_CUTE80_dataset.html) | - | [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/ct80/test_label.txt) | |
| svt |[下载地址](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 | [非官方下载地址*](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) | [下载地址](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) | [下载地址](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 | [下载地址](https://textvqa.org/textocr/dataset) | - | - | |
| Totaltext | [下载地址](https://github.com/cs-chan/Total-Text-Dataset) | - | - | |
| OpenVINO | [下载地址](https://github.com/cvdfoundation/open-images-dataset) | [下载地址](https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text) |[下载地址](https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text)| |
(*) 注：由于官方的下载地址已经无法访问，我们提供了一个非官方的地址以供参考，但我们无法保证数据的准确性。
## 准备步骤
### ICDAR 2013
- 第一步：从 [下载地址](https://rrc.cvc.uab.es/?ch=2&com=downloads) 下载 `Challenge2_Test_Task3_Images.zip` 和 `Challenge2_Training_Task3_Images_GT.zip`
- 第二步：下载 [test_label_1015.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/test_label_1015.txt) 和 [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/train_label.txt)
### ICDAR 2015
- 第一步：从 [下载地址](https://rrc.cvc.uab.es/?ch=4&com=downloads) 下载 `ch4_training_word_images_gt.zip` 和 `ch4_test_word_images_gt.zip`
- 第二步：下载 [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
- 第一步：从 [下载地址](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html) 下载 `IIIT5K-Word_V3.0.tar.gz`
- 第二步：下载 [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/train_label.txt) 和 [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/test_label.txt)
### svt
- 第一步：从 [下载地址](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset) 下载 `svt.zip`
- 第二步：下载 [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/svt/test_label.txt)
- 第三步：
```bash
python tools/data/textrecog/svt_converter.py <download_svt_dir_path>
```
### ct80
- 第一步：下载 [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/ct80/test_label.txt)
### svtp
- 第一步：下载 [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/svtp/test_label.txt)
### coco_text
- 第一步：从 [下载地址](https://rrc.cvc.uab.es/?ch=5&com=downloads) 下载文件
- 第二步：下载 [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/coco_text/train_label.txt)
### MJSynth (Syn90k)
- 第一步：从 [下载地址](https://www.robots.ox.ac.uk/~vgg/data/text/) 下载 `mjsynth.tar.gz`
- 第二步：下载 [shuffle_labels.txt](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/shuffle_labels.txt)
- 第三步：
```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 .
# 创建软链接
cd /path/to/mmocr/data/mixture
ln -s /path/to/Syn90k Syn90k
```
### SynthText (Synth800k)
- 第一步：下载 `SynthText.zip`: [下载地址](https://www.robots.ox.ac.uk/~vgg/data/scenetext/)
- 第二步：请根据你的实际需要，从下列标注中选择最适合的下载：[label.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/label.txt) （7,266,686个标注）； [shuffle_labels.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/shuffle_labels.txt) （2,400,000个随机采样的标注）；[alphanumeric_labels.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/alphanumeric_labels.txt) （7,239,272个仅包含数字和字母的标注）；[instances_train.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/instances_train.txt) （7,266,686个字符级别的标注）。
- 第三步：
```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 .
# 创建软链接
cd /path/to/mmocr/data/mixture
ln -s /path/to/SynthText SynthText
```
- 第四步：生成裁剪后的图像和标注：
```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
- 第一步：从 [SynthAdd](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg) (code:627x) 下载 `SynthText_Add.zip`
- 第二步：下载 [label.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthAdd/label.txt)
- 第三步：
```bash
mkdir SynthAdd && cd SynthAdd
mv /path/to/SynthText_Add.zip .
unzip SynthText_Add.zip
mv /path/to/label.txt .
# 创建软链接
cd /path/to/mmocr/data/mixture
ln -s /path/to/SynthAdd SynthAdd
```
:::{tip}
运行以下命令，可以把 `.txt` 格式的标注文件转换成 `.lmdb` 格式：
```bash
python tools/data/utils/txt2lmdb.py -i <txt_label_path> -o <lmdb_label_path>
```
例如：
```bash
python tools/data/utils/txt2lmdb.py -i data/mixture/Syn90k/label.txt -o data/mixture/Syn90k/label.lmdb
```
:::
### TextOCR
- 第一步：下载 [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) 和 [TextOCR_0.1_val.json](https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_val.json) 到 `textocr/` 目录.
```bash
mkdir textocr && cd textocr
# 下载 TextOCR 数据集
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
# 对于数据图像
unzip -q train_val_images.zip
mv train_images train
```
- 第二步：用四个并行进程剪裁图像然后生成 `train_label.txt`，`val_label.txt` ，可以使用以下命令：
```bash
python tools/data/textrecog/textocr_converter.py /path/to/textocr 4
```
### Totaltext
- 第一步：从 [github dataset](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset) 下载 `totaltext.zip`，然后从 [github Groundtruth](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Groundtruth/Text) 下载 `groundtruth_text.zip` （我们建议下载 `.mat` 格式的标注文件，因为我们提供的 `totaltext_converter.py` 标注格式转换工具只支持 `.mat` 文件）
```bash
mkdir totaltext && cd totaltext
mkdir imgs && mkdir annotations
# 对于图像数据
# 在 ./totaltext 目录下运行
unzip totaltext.zip
mv Images/Train imgs/training
mv Images/Test imgs/test
# 对于标注文件
unzip groundtruth_text.zip
cd Groundtruth
mv Polygon/Train ../annotations/training
mv Polygon/Test ../annotations/test
```
- 第二步：用以下命令生成经剪裁后的标注文件 `train_label.txt` 和 `test_label.txt` （剪裁后的图像会被保存在目录 `data/totaltext/dst_imgs/`）：
```bash
python tools/data/textrecog/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
```
### OpenVINO
- 第零步：安装 [awscli](https://aws.amazon.com/cli/)。
- 第一步：下载 [Open Images](https://github.com/cvdfoundation/open-images-dataset#download-images-with-bounding-boxes-annotations) 的子数据集 `train_1`、 `train_2`、 `train_5`、 `train_f` 及 `validation` 至 `openvino/`。
```bash
mkdir openvino && cd openvino
# 下载 Open Images 的子数据集
for s in 1 2 5 f; do
aws s3 --no-sign-request cp s3://open-images-dataset/tar/train_${s}.tar.gz .
done
aws s3 --no-sign-request cp s3://open-images-dataset/tar/validation.tar.gz .
# 下载标注文件
for s in 1 2 5 f; do
wget https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text/text_spotting_openimages_v5_train_${s}.json
done
wget https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text/text_spotting_openimages_v5_validation.json
# 解压数据集
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
```
- 第二步： 运行以下的命令，以用4个进程生成标注 `train_{1,2,5,f}_label.txt` 和 `val_label.txt` 并裁剪原图：
```bash
python tools/data/textrecog/openvino_converter.py /path/to/openvino 4
```
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