Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/MhLiao/MaskTextSpotter
A PyTorch implementation of Mask TextSpotter
https://github.com/MhLiao/MaskTextSpotter
scene-text-detection-recognition
Last synced: about 2 months ago
JSON representation
A PyTorch implementation of Mask TextSpotter
- Host: GitHub
- URL: https://github.com/MhLiao/MaskTextSpotter
- Owner: MhLiao
- Created: 2019-10-06T07:42:55.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-01-27T15:15:46.000Z (almost 4 years ago)
- Last Synced: 2024-08-02T11:14:52.644Z (5 months ago)
- Topics: scene-text-detection-recognition
- Language: Python
- Homepage: https://github.com/MhLiao/MaskTextSpotter
- Size: 312 KB
- Stars: 415
- Watchers: 17
- Forks: 96
- Open Issues: 42
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MaskTextSpotter
This is the code of "Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes" (TPAMI version).
It is an extension of the ECCV version while sharing the same title. For more details, please refer to our [TPAMI paper](https://ieeexplore.ieee.org/document/8812908).
This repo is inherited from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and follows the same license.
## ToDo List
- [x] Release code
- [x] Document for Installation
- [x] Trained models
- [x] Document for testing
- [x] Document for training
- [x] Demo script
- [x] Evaluation
- [ ] Release the standalone recognition model
## Installation
### Requirements:
- Python3 (Python3.7 is recommended)
- PyTorch >= 1.0 (1.2 is recommended)
- torchvision from master
- cocoapi
- yacs
- matplotlib
- GCC >= 4.9 (This is very important!)
- OpenCV
- CUDA >= 9.0 (10.0 is recommended)
```bash
# first, make sure that your conda is setup properly with the right environment
# for that, check that `which conda`, `which pip` and `which python` points to the
# right path. From a clean conda env, this is what you need to do
conda create --name masktextspotter -y
conda activate masktextspotter
# this installs the right pip and dependencies for the fresh python
conda install ipython pip
# python dependencies
pip install ninja yacs cython matplotlib tqdm opencv-python shapely scipy tensorboardX
# install PyTorch
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
export INSTALL_DIR=$PWD
# install pycocotools
cd $INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install
# install apex (optional)
cd $INSTALL_DIR
git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext
# clone repo
cd $INSTALL_DIR
git clone https://github.com/MhLiao/MaskTextSpotter.git
cd MaskTextSpotter
# build
python setup.py build develop
unset INSTALL_DIR
```
## Models
Download Trained [model](https://drive.google.com/open?id=1pPRS7qS_K1keXjSye0kksqhvoyD0SARz)
## Demo
You can run a demo script for a single image inference by ```python tools/demo.py```.
## Datasets
Download the ICDAR2013([Google Drive](https://drive.google.com/open?id=1sptDnAomQHFVZbjvnWt2uBvyeJ-gEl-A), [BaiduYun](https://pan.baidu.com/s/18W2aFe_qOH8YQUDg4OMZdw)) and ICDAR2015([Google Drive](https://drive.google.com/open?id=1HZ4Pbx6TM9cXO3gDyV04A4Gn9fTf2b5X), [BaiduYun](https://pan.baidu.com/s/16GzPPzC5kXpdgOB_76A3cA)) as examples.
The SCUT dataset used for training can be downloaded [here](https://drive.google.com/file/d/1BpE2GEFF7Ay7jPqgaeHxMmlXvM-1Es5_/view?usp=sharing).
The converted labels of Total-Text dataset can be downloaded [here](https://1drv.ms/u/s!ArsnjfK83FbXgcpti8Zq9jSzhoQrqw?e=99fukk).
The converted labels of SynthText can be downloaded [here](https://1drv.ms/u/s!ArsnjfK83FbXgb5vgOOVPYywgCWuQw?e=UPuNTa).
The root of the dataset directory should be ```MaskTextSpotter/datasets/```.
## Testing
### Prepar dataset
An example of the path of test images: ```MaskTextSpotter/datasets/icdar2015/test_iamges```
### Check the config file (configs/finetune.yaml) for some parameters.
test dataset: ```TEST.DATASETS```;
input size: ```INPUT.MIN_SIZE_TEST''';
model path: ```MODEL.WEIGHT```;
output directory: ```OUTPUT_DIR```
### run ```sh test.sh```
## Training
Place all the training sets in ```MaskTextSpotter/datasets/``` and check ```DATASETS.TRAIN``` in the config file.
### Pretrain
Trained with SynthText
```python3 -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file configs/pretrain.yaml ```
### Finetune
Trained with a mixure of SynthText, icdar2013, icdar2015, scut-eng-char, and total-text
check the initial weights in the config file.
```python3 -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file configs/finetune.yaml ```
## Evaluation
### Evaluation for ICDAR 2015 dataset
download the [lexicons](https://drive.google.com/open?id=1u3NlpIZkE4dYmrcWo0qzU_q7ra5jvDhD) and place them like ```evaluation/lexicons/ic15/```
```
cd evaluation/icdar2015/e2e/
# edit "result_dir" in script.py
python script.py
```
### Evaluation for Total-Text dataset (ToDo)
## Citing the related works
Please cite the related works in your publications if it helps your research:
@article{liao2019mask,
author={M. {Liao} and P. {Lyu} and M. {He} and C. {Yao} and W. {Wu} and X. {Bai}},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes},
year={2021},
volume={43},
number={2},
pages={532-548},
doi={10.1109/TPAMI.2019.2937086}}
}
@inproceedings{lyu2018mask,
title={Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes},
author={Lyu, Pengyuan and Liao, Minghui and Yao, Cong and Wu, Wenhao and Bai, Xiang},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
pages={67--83},
year={2018}
}