# 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 ``` ### 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 -o ``` 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 ```