Brush Calligraphy Stroke Segmentation Dataset (BCSS)

Introduction

The Brush Calligraphy Stroke Segmentation Dataset (BCSS) is a comprehensive resource for the task of brush calligraphy stroke segmentation. It is derived from the Evaluated Chinese Calligraphy Copies (E3C) dataset ¹, an aesthetic evaluation dataset for Chinese brush calligraphy, and augmented with additional images from diverse sources. This expansion enhances the dataset's diversity and supports the evaluation of model generalization.

Dataset Structure

The BCSS dataset consists of 1,322 images and 10,653 annotated strokes, distributed across the following subsets:

• Training and Validation Set: 1,022 images from the E3C dataset.
• External Testing Set: 300 images, including:
  • 90 images from the E3C dataset, containing character types not seen in the training and validation sets.
  • 113 handwritten images extracted from the CCSE-W dataset ².
  • 97 images representing various Chinese character styles, including regular printed and brush calligraphy forms, such as Clerical Script.

Applications

BCSS can be used to train and evaluate models for brush calligraphy stroke segmentation. It offers a rich variety of Chinese character styles and a comprehensive testing set, which enables the evaluation of model generalization capabilities across different writing styles.

The modeling directory contains sample code for the task, implemented using a Fully Convolutional Network (FCN) ³. The model proposed in our paper is built on the DeepLab v3 framework ⁴, and further modifications can be made based on the Stroke-Seg paper details.

Dataset Access

The BCSS dataset is publicly available for research purposes. Some raw data can be found in the instances directory, while the annotation source files for template characters are located in the labels directory. These annotations are provided for reference and formatting purposes.

Contact

For inquiries or additional information about the dataset, please contact:

• zeyangbai.rvo@gmail.com
• xiebin@csu.edu.cn

References

Citation

If you use our dataset, code, or methods, please cite the following paper:

@article{gong2024stroke,
  title={Stroke-Seg: A Deep Learning-Based Framework for Chinese Stroke Segmentation},
  author={Gong, Xinyu and Bai, Zeyang and Nie, Haitao and Xie, Bin},
  journal={IET Image Processing},
  volume={18},
  number={13},
  pages={4341--4355},
  year={2024},
  publisher={Wiley Online Library}
}

Footnotes

1. Sun, M., et al. (2023). SRAFE: Siamese Regression Aesthetic Fusion Evaluation for Chinese Calligraphic Copy. CAAI Transactions on Intelligent Technology, 8(3), 1077–1086. ↩

2. Liu, L., Lin, K., Huang, S., Li, Z., Li, C., Cao, Y., & Zhou, Q. (2022). Instance Segmentation for Chinese Character Stroke Extraction: Datasets and Benchmarks. arXiv, 2210.13826. ↩

3. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully Convolutional Networks for Semantic Segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3431-3440). ↩

4. Chen, L. C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv preprint arXiv:1706.05587. ↩