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Overview of our SDT
Three samples of online characters with writing orders
Online Chinese handwriting generation
Applications to various scripts
Extension on offline Chinese handwriting generation
conda create -n sdt python=3.8 -y
conda activate sdt
# install all dependencies
pip install -r requirements.txt
SDT/
β
βββ train.py - main script to start training
βββ test.py - generate characters via trained model
βββ evaluate.py - evaluation of generated samples
β
βββ configs/*.yml - holds configuration for training
βββ parse_config.py - class to handle config file
β
βββ data_loader/ - anything about data loading goes here
β βββ loader.py
β
βββ model_zoo/ - pre-trained content encoder model
β
βββ data/ - default directory for storing experimental datasets
β
βββ model/ - networks, models and losses
β βββ encoder.py
β βββ gmm.py
β βββ loss.py
β βββ model.py
β βββ transformer.py
β
βββ saved/
β βββ models/ - trained models are saved here
β βββ tborad/ - tensorboard visualization
β βββ samples/ - visualization samples in the training process
β
βββ trainer/ - trainers
β βββ trainer.py
β
βββ utils/ - small utility functions
βββ util.py
βββ logger.py - set log dir for tensorboard and logging output
We provide Chinese, Japanese and English datasets in Google Drive | Baidu Netdisk PW:xu9u. Please download these datasets, uzip them and move the extracted files to /data.
Training
To train the SDT on the Chinese dataset, run this command:
python train.py --cfg configs/CHINESE_CASIA.yml --log Chinese_log
To train the SDT on the Japanese dataset, run this command:
python train.py --cfg configs/Japanese_TUATHANDS.yml --log Japanese_log
To train the SDT on the English dataset, run this command:
python train.py --cfg configs/English_CASIA.yml --log English_log
Qualitative Test
To generate Chinese handwritings with our SDT, run this command:
python test.py --pretrained_model checkpoint_path --store_type online --sample_size 500 --dir Generated/Chinese
To generate Japanese handwritings with our SDT, run this command:
python test.py --pretrained_model checkpoint_path --store_type online --sample_size 500 --dir Generated/Japanese
To generate English handwritings with our SDT, run this command:
python test.py --pretrained_model checkpoint_path --store_type online --sample_size 500 --dir Generated/English
Quantitative Evaluation
data_path
to the path of the generated handwritings (e.g., Generated/Chinese), and run this command:
python evaluate.py --data_path Generated/Chinese
We are delighted to discover that P0etry-rain has proposed a pipeline that involves initially converting the generated results by our SDT to TTF format, followed by the development of software to enable flexible adjustments in spacing between paragraphs, lines, and characters. Below, we present TTF files, software interface and the printed results. More details can be seen in #78.
TTF File
Software Interface
Printed Results
If you find our work inspiring or use our codebase in your research, please cite our work:
@inproceedings{dai2023disentangling,
title={Disentangling Writer and Character Styles for Handwriting Generation},
author={Dai, Gang and Zhang, Yifan and Wang, Qingfeng and Du, Qing and Yu, Zhuliang and Liu, Zhuoman and Huang, Shuangping},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,
pages={5977--5986},
year={2023}
}