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Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks.

Created by

Lizhao Liu, Kunyang Lin, Shangxin Huang from South China University of Technology;

Qingyu Zhou, Zhongli Li from Tencent Cloud Xiaowei;

Chao Li from Xiaomi Group.

This repository contains the official PyTorch-implementation of our paper Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks.

In particular, we release the code for reproducing the CNN-related results in the main paper.

Suggestions are always welcome!

Datasets preparation.

Note: Both datasets are annotated with the standard COCO format.

Datasets visualization

Datasets Statistics

How to use?

  • Install required package

    • Install detectron2 pre-compiled version (require CUDA>=10.2, torch==1.7)
    python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.7/index.html
    • Install requirements.txt
    pip install -r requirements.txt

  • Config related environments field for the used config files below

    • Set the DATA_ROOT to your datasets placing path
    • Set the GPU_IDS to your machine GPU IDs. Support multiple GPU, for example [0, 1].
    • Set the OUTPUT_ID to your experiment name.
    • Set the OUTPUT_DIR. The output files (checkpoint, log, tensorboard) are located at OUTPUT_DIR/OUTPUT_ID/{timestamp}/

  • Train a model

    • Template
    PYTHONPATH=$PYTHONPATH:./ python -u scripts/{your_train_script}.py --config {path_to_your_train_config}/{your_train_config}.yaml
    • To train a handwritten Chinese stroke instance segmentation model
    PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_handwritten.yaml
    • To train a kaiti Chinese stroke instance segmentation model
    PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_kaiti.yaml

  • Inference (or test) a model

    • Set the MODEL.WEIGHTS to your trained ckpt path
    • Optional: Set IMAGE_PATHS for individual image results visualization
    • Optional: Set VIS_DATASET_RESULT for val/test results visualization
    • Template
    PYTHONPATH=$PYTHONPATH:./ python -u scripts/{your_inference_script}.py --config {path_to_your_inference_config}/{your_inference_config}.yaml
    • To inference a handwritten Chinese stroke instance segmentation model
    PYTHONPATH=$PYTHONPATH:./ python -u scripts/inference_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_handwritten_test.yaml
    • To inference a kaiti Chinese stroke instance segmentation model
    PYTHONPATH=$PYTHONPATH:./ python -u scripts/inference_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_kaiti_test.yaml

  • Monitor the training process

cd ``OUTPUT_DIR`` # your output dir
tensorboard --logdir=./ --port=6006 --bind_all
  • To reproduce the results in the paper
All commands are recorded in terminal_new.record file. Go check out.

Project Structure

  • common contains the utils functions that will be used across the whole projects
  • dataset contains the dataloader used for training or inference for each task
  • initializer contains the initializer (init env, log, output dir, etc.) for each task
  • module contains the module will be used by some models or training/inference scripts
  • pre_process contains the preprocessing code to preprocess the data
  • scripts contains the entrance of training or inference a model

How to add your new model to train (or inference)?

  • Step 1: add the new model into module package
  • Step 2: add the specific initializer into initializer package
  • Step 3: add the training (or inference) into scripts package

Quantitative Results

Qualitative Results

Acknowledgement

Our codebase is based on detectron2. Please show some support!

Citation

If you find this code helpful for your research, please consider citing

@article{liu2022instance,
  title={Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks},
  author={Liu, Lizhao and Lin, Kunyang and Huang, Shangxin and Li, Zhongli and Li, Chao and Cao, Yunbo and Zhou, Qingyu},
  journal={arXiv preprint arXiv:2210.13826},
  year={2022}
}