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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 | |
| ``` | |