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* Add FUNSD Converter * Update tools/data/textrecog/funsd_converter.py Co-authored-by: Tong Gao <gaotongxiao@gmail.com> * Update tools/data/textrecog/funsd_converter.py Co-authored-by: Tong Gao <gaotongxiao@gmail.com> * Update tools/data/textdet/funsd_converter.py Co-authored-by: Tong Gao <gaotongxiao@gmail.com> * blank line between sections Co-authored-by: Tong Gao <gaotongxiao@gmail.com> * fix incorrect docstrings * fix docstrings & fix timer * add --preserve-vertical arg for preserving vertical texts * fix --preserve-vertical * [doc] fix recog.md incorrect description * fix docstring style Co-authored-by: Tong Gao <gaotongxiao@gmail.com> * fix docstring spaces Co-authored-by: Tong Gao <gaotongxiao@gmail.com>
215 lines
15 KiB
Markdown
215 lines
15 KiB
Markdown
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# Text Detection
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## Overview
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The structure of the text detection dataset directory is organized as follows.
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```text
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├── ctw1500
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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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├── icdar2015
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│ ├── imgs
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│ ├── instances_test.json
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│ └── instances_training.json
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├── icdar2017
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│ ├── imgs
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│ ├── instances_training.json
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│ └── instances_val.json
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├── synthtext
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│ ├── imgs
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│ └── instances_training.lmdb
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│ ├── data.mdb
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│ └── lock.mdb
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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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├── totaltext
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│ ├── imgs
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│ ├── instances_test.json
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│ └── instances_training.json
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├── CurvedSynText150k
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│ ├── syntext_word_eng
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│ ├── emcs_imgs
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│ └── instances_training.json
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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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| 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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| 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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## Important Note
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:::{note}
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**For users who want to train models on CTW1500, ICDAR 2015/2017, and Totaltext dataset,** there might be some images containing orientation info in EXIF data. The default OpenCV
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backend used in MMCV would read them and apply the rotation on the images. However, their gold annotations are made on the raw pixels, and such
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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)
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:::
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## Preparation Steps
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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 following command:
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```bash
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python tools/data/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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### ICDAR 2017
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- Follow similar steps as [ICDAR 2015](#icdar-2015).
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### CTW1500
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- Step0: Read [Important Note](#important-note)
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- Step1: Download `train_images.zip`, `test_images.zip`, `train_labels.zip`, `test_labels.zip` from [github](https://github.com/Yuliang-Liu/Curve-Text-Detector)
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```bash
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mkdir ctw1500 && cd ctw1500
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mkdir imgs && mkdir annotations
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# For annotations
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cd annotations
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wget -O train_labels.zip https://universityofadelaide.box.com/shared/static/jikuazluzyj4lq6umzei7m2ppmt3afyw.zip
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wget -O test_labels.zip https://cloudstor.aarnet.edu.au/plus/s/uoeFl0pCN9BOCN5/download
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unzip train_labels.zip && mv ctw1500_train_labels training
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unzip test_labels.zip -d test
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cd ..
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# For images
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cd imgs
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wget -O train_images.zip https://universityofadelaide.box.com/shared/static/py5uwlfyyytbb2pxzq9czvu6fuqbjdh8.zip
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wget -O test_images.zip https://universityofadelaide.box.com/shared/static/t4w48ofnqkdw7jyc4t11nsukoeqk9c3d.zip
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unzip train_images.zip && mv train_images training
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unzip test_images.zip && mv test_images test
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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/data/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1500 --split-list training test
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```
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### SynthText
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- 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/`.
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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/data/textdet/textocr_converter.py /path/to/textocr
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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` 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 annotations
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unzip groundtruth_text.zip
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cd Groundtruth
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mv Polygon/Train ../annotations/training
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mv Polygon/Test ../annotations/test
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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/data/textdet/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
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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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- Step2:
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```bash
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unzip -q syntext1.zip
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mv train.json train1.json
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unzip images.zip
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rm images.zip
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unzip -q syntext2.zip
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mv train.json train2.json
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unzip images.zip
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rm images.zip
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```
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- Step3: Download [instances_training.json](https://download.openmmlab.com/mmocr/data/curvedsyntext/instances_training.json) to `CurvedSynText150k/`
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- Or, generate `instances_training.json` with following command:
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```bash
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python tools/data/common/curvedsyntext_converter.py PATH/TO/CurvedSynText150k --nproc 4
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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/data/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
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```
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