mirror of https://github.com/open-mmlab/mmocr.git
[Docs] Remove unsupported datasets in docs (#1670)
Tong Gao
GitHub
parent
3b0a41518d
commit
03a23ca4db
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@ -33,7 +33,7 @@ Also, the script supports preparing multiple datasets at the same time. For exam
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python tools/dataset_converters/prepare_dataset.py icdar2015 totaltext --task textrecog
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```
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To check the supported datasets in MMOCR, please refer to [Dataset Zoo](./datasetzoo.md).
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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).
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## Advanced Usage
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@ -1,40 +1,32 @@
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# Text Detection
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```{note}
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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)!
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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.
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```
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## Overview
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| Dataset | Images | | Annotation Files | | |
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| :---------------: | :-------------------------------------------: | :-------------------------------------: | :------------------------------------------------------: | :--------------------------------------: | :-: |
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| | | training | validation | testing | |
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| CTW1500 | [homepage](https://github.com/Yuliang-Liu/Curve-Text-Detector) | - | - | - | |
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| ICDAR2011 | [homepage](https://rrc.cvc.uab.es/?ch=1) | - | - | | |
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| ICDAR2013 | [homepage](https://rrc.cvc.uab.es/?ch=2) | - | - | - | |
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| 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) | |
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| 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) | - | |
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| 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)) | - | - | |
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| TextOCR | [homepage](https://textvqa.org/textocr/dataset) | - | - | - | |
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| Totaltext | [homepage](https://github.com/cs-chan/Total-Text-Dataset) | - | - | - | |
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| 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) | - | - | |
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| FUNSD | [homepage](https://guillaumejaume.github.io/FUNSD/) | - | - | - | |
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| DeText | [homepage](https://rrc.cvc.uab.es/?ch=9) | - | - | - | |
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| NAF | [homepage](https://github.com/herobd/NAF_dataset/releases/tag/v1.0) | - | - | - | |
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| SROIE | [homepage](https://rrc.cvc.uab.es/?ch=13) | - | - | - | |
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| Lecture Video DB | [homepage](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb) | - | - | - | |
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| LSVT | [homepage](https://rrc.cvc.uab.es/?ch=16) | - | - | - | |
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| IMGUR | [homepage](https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset) | - | - | - | |
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| KAIST | [homepage](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database) | - | - | - | |
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| MTWI | [homepage](https://tianchi.aliyun.com/competition/entrance/231685/information?lang=en-us) | - | - | - | |
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| COCO Text v2 | [homepage](https://bgshih.github.io/cocotext/) | - | - | - | |
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| ReCTS | [homepage](https://rrc.cvc.uab.es/?ch=12) | - | - | - | |
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| IIIT-ILST | [homepage](http://cvit.iiit.ac.in/research/projects/cvit-projects/iiit-ilst) | - | - | - | |
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| VinText | [homepage](https://github.com/VinAIResearch/dict-guided) | - | - | - | |
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| BID | [homepage](https://github.com/ricardobnjunior/Brazilian-Identity-Document-Dataset) | - | - | - | |
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| RCTW | [homepage](https://rctw.vlrlab.net/index.html) | - | - | - | |
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| HierText | [homepage](https://github.com/google-research-datasets/hiertext) | - | - | - | |
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| ArT | [homepage](https://rrc.cvc.uab.es/?ch=14) | - | - | - | |
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| Dataset | Images | | Annotation Files | | |
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| :---------------: | :------------------------------------------------------: | :------------------------------------------------: | :-----------------------------------------------------------------: | :-----: | :-: |
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| | | training | validation | testing | |
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| CTW1500 | [homepage](https://github.com/Yuliang-Liu/Curve-Text-Detector) | - | - | - | |
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| ICDAR2011 | [homepage](https://rrc.cvc.uab.es/?ch=1) | - | - | | |
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| 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) | - | |
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| 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)) | - | - | |
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| 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) | - | - | |
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| DeText | [homepage](https://rrc.cvc.uab.es/?ch=9) | - | - | - | |
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| Lecture Video DB | [homepage](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb) | - | - | - | |
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| LSVT | [homepage](https://rrc.cvc.uab.es/?ch=16) | - | - | - | |
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| IMGUR | [homepage](https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset) | - | - | - | |
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| KAIST | [homepage](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database) | - | - | - | |
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| MTWI | [homepage](https://tianchi.aliyun.com/competition/entrance/231685/information?lang=en-us) | - | - | - | |
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| ReCTS | [homepage](https://rrc.cvc.uab.es/?ch=12) | - | - | - | |
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| IIIT-ILST | [homepage](http://cvit.iiit.ac.in/research/projects/cvit-projects/iiit-ilst) | - | - | - | |
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| VinText | [homepage](https://github.com/VinAIResearch/dict-guided) | - | - | - | |
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| BID | [homepage](https://github.com/ricardobnjunior/Brazilian-Identity-Document-Dataset) | - | - | - | |
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| RCTW | [homepage](https://rctw.vlrlab.net/index.html) | - | - | - | |
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| HierText | [homepage](https://github.com/google-research-datasets/hiertext) | - | - | - | |
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| ArT | [homepage](https://rrc.cvc.uab.es/?ch=14) | - | - | - | |
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### Install AWS CLI (optional)
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@ -142,82 +134,6 @@ inconsistency results in false examples in the training set. Therefore, users sh
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│ └── instances_training.json
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```
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## ICDAR 2013 (Focused Scene Text)
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- 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)`.
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```bash
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mkdir icdar2013 && cd icdar2013
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mkdir imgs && mkdir annotations
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# Download ICDAR 2013
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wget https://rrc.cvc.uab.es/downloads/Challenge2_Training_Task12_Images.zip --no-check-certificate
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wget https://rrc.cvc.uab.es/downloads/Challenge2_Test_Task12_Images.zip --no-check-certificate
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wget https://rrc.cvc.uab.es/downloads/Challenge2_Training_Task1_GT.zip --no-check-certificate
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wget https://rrc.cvc.uab.es/downloads/Challenge2_Test_Task1_GT.zip --no-check-certificate
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# For images
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unzip -q Challenge2_Training_Task12_Images.zip -d imgs/training
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unzip -q Challenge2_Test_Task12_Images.zip -d imgs/test
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# For annotations
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unzip -q Challenge2_Training_Task1_GT.zip -d annotations/training
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unzip -q Challenge2_Test_Task1_GT.zip -d annotations/test
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rm Challenge2_Training_Task12_Images.zip && rm Challenge2_Test_Task12_Images.zip && rm Challenge2_Training_Task1_GT.zip && rm Challenge2_Test_Task1_GT.zip
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```
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- Step 2: Generate `instances_training.json` and `instances_test.json` with the following command:
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```bash
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python tools/dataset_converters/textdet/ic13_converter.py PATH/TO/icdar2013 --nproc 4
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```
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- After running the above codes, the directory structure should be as follows:
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```text
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│── icdar2013
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│ ├── imgs
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│ ├── instances_test.json
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│ └── instances_training.json
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```
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## ICDAR 2015
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- Step0: Read [Important Note](#important-note)
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- 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)
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- Step2:
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```bash
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mkdir icdar2015 && cd icdar2015
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mkdir imgs && mkdir annotations
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# For images,
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mv ch4_training_images imgs/training
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mv ch4_test_images imgs/test
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# For annotations,
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mv ch4_training_localization_transcription_gt annotations/training
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mv Challenge4_Test_Task1_GT annotations/test
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```
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- 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`
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- Or, generate `instances_training.json` and `instances_test.json` with the following command:
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```bash
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python tools/dataset_converters/textdet/icdar_converter.py /path/to/icdar2015 -o /path/to/icdar2015 -d icdar2015 --split-list training test
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```
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- The resulting directory structure looks like the following:
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```text
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├── icdar2015
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│ ├── imgs
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│ ├── annotations
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│ ├── instances_test.json
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│ └── instances_training.json
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```
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## ICDAR 2017
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- Follow similar steps as [ICDAR 2015](#icdar-2015).
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@ -248,81 +164,6 @@ inconsistency results in false examples in the training set. Therefore, users sh
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│ └── lock.mdb
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```
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## TextOCR
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- 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/`.
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```bash
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mkdir textocr && cd textocr
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# Download TextOCR dataset
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wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip
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wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_train.json
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wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_val.json
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# For images
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unzip -q train_val_images.zip
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mv train_images train
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```
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- Step2: Generate `instances_training.json` and `instances_val.json` with the following command:
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```bash
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python tools/dataset_converters/textdet/textocr_converter.py /path/to/textocr
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```
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- The resulting directory structure looks like the following:
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```text
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├── textocr
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│ ├── train
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│ ├── instances_training.json
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│ └── instances_val.json
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```
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## Totaltext
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- Step0: Read [Important Note](#important-note)
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- 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).
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```bash
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mkdir totaltext && cd totaltext
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mkdir imgs && mkdir annotations
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# For images
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# in ./totaltext
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unzip totaltext.zip
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mv Images/Train imgs/training
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mv Images/Test imgs/test
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# For legacy training and test annotations
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unzip groundtruth_text.zip
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mv Groundtruth/Polygon/Train annotations/training
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mv Groundtruth/Polygon/Test annotations/test
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# Using the latest training annotations
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# WARNING: Delete legacy train annotations before running the following command.
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unzip TT_new_train_GT.zip
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mv Train annotations/training
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```
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- Step2: Generate `instances_training.json` and `instances_test.json` with the following command:
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```bash
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python tools/dataset_converters/textdet/totaltext_converter.py /path/to/totaltext
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```
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- The resulting directory structure looks like the following:
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```text
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├── totaltext
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│ ├── imgs
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│ ├── annotations
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│ ├── instances_test.json
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│ └── instances_training.json
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```
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## CurvedSynText150k
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- 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/`.
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@ -358,43 +199,6 @@ inconsistency results in false examples in the training set. Therefore, users sh
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│ └── instances_training.json
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```
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## FUNSD
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- Step1: Download [dataset.zip](https://guillaumejaume.github.io/FUNSD/dataset.zip) to `funsd/`.
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```bash
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mkdir funsd && cd funsd
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# Download FUNSD dataset
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wget https://guillaumejaume.github.io/FUNSD/dataset.zip
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unzip -q dataset.zip
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# For images
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mv dataset/training_data/images imgs && mv dataset/testing_data/images/* imgs/
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# For annotations
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mkdir annotations
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mv dataset/training_data/annotations annotations/training && mv dataset/testing_data/annotations annotations/test
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rm dataset.zip && rm -rf dataset
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```
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- Step2: Generate `instances_training.json` and `instances_test.json` with following command:
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```bash
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python tools/dataset_converters/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
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```
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- The resulting directory structure looks like the following:
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```text
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│── funsd
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│ ├── annotations
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│ ├── imgs
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│ ├── instances_test.json
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│ └── instances_training.json
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```
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## DeText
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- 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).
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@ -432,84 +236,6 @@ inconsistency results in false examples in the training set. Therefore, users sh
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│ └── instances_training.json
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```
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## NAF
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- Step1: Download [labeled_images.tar.gz](https://github.com/herobd/NAF_dataset/releases/tag/v1.0) to `naf/`.
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```bash
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mkdir naf && cd naf
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# Download NAF dataset
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wget https://github.com/herobd/NAF_dataset/releases/download/v1.0/labeled_images.tar.gz
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tar -zxf labeled_images.tar.gz
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# For images
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mkdir annotations && mv labeled_images imgs
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# For annotations
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git clone https://github.com/herobd/NAF_dataset.git
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mv NAF_dataset/train_valid_test_split.json annotations/ && mv NAF_dataset/groups annotations/
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rm -rf NAF_dataset && rm labeled_images.tar.gz
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```
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- Step2: Generate `instances_training.json`, `instances_val.json`, and `instances_test.json` with following command:
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```bash
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python tools/dataset_converters/textdet/naf_converter.py PATH/TO/naf --nproc 4
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```
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- After running the above codes, the directory structure should be as follows:
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```text
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│── naf
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│ ├── annotations
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│ ├── imgs
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│ ├── instances_test.json
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│ ├── instances_val.json
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│ └── instances_training.json
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```
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## SROIE
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|
||||
- 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).
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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}
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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) |
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
Loading…
Reference in New Issue