Box_Discretization_Network

This repository is built on the pytorch [maskrcnn_benchmark].

Description

Paper [link].

This method is served as the foundation for our recent ICDAR 2019 ReCTs competition method [link], which won the first place of the detection task.

Getting Started

A basic guide for train and test.

Install anaconda

Link：https://pan.baidu.com/s/1TGy6O3LBHGQFzC20yJo8tg psw：vggx

Step-by-step install

conda create --name mb
conda activate mb
conda install ipython
pip install ninja yacs cython matplotlib tqdm scipy shapely
conda install pytorch=1.0 torchvision=0.2 cudatoolkit=9.0 -c pytorch
conda install -c menpo opencv
export INSTALL_DIR=$PWD
cd $INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install
cd $INSTALL_DIR
git clone https://github.com/Yuliang-Liu/Box_Discretization_Network.git
cd Box_Discretization_Network
python setup.py build develop

Pretrained model：

[Link] unzip under project_root

ic15 data

Prepare data follow COCO format. [Link] unzip under datasets/

Train

After downloading data and pretrained model, run

bash quick_train_guide.sh

Test with [TIoU]

Run

bash my_test.sh

Put kes.json to ic15_TIoU_metric/ inside ic15_TIoU_metric/

Run (conda deactivate; pip install Polygon2)

python2 to_eval.py

Visualization

Run

bash single_image_demo.sh

Citation

If you find our metric useful for your reserach, please cite

@article{liu2019omnidirectional,
  title={Omnidirectional Scene Text Detection with Sequential-free Box Discretization},
  author={Liu, Yuliang and Zhang, Sheng and Jin, Lianwen and Xie, Lele and Wu, Yaqiang and Wang, Zhepeng},
  journal={IJCAI},
  year={2019}
}

Feedback

Suggestions and discussions are greatly welcome. Please contact the authors by sending email to liu.yuliang@mail.scut.edu.cn or yuliang.liu@adelaide.edu.au. For non-commercial usage, please contact Prof. Lianwen Jin via lianwen.jin@gmail.com.