Add BCSS dataset (ZIP archives + metadata) and dataset card

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Zeyang Bai
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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 ---
 license: mit
+task_categories:
+  - image-segmentation
+language:
+  - zh
+tags:
+  - chinese-calligraphy
+  - stroke-segmentation
+  - brush-calligraphy
+  - handwriting
+  - semantic-segmentation
+pretty_name: Brush Calligraphy Stroke Segmentation (BCSS)
+size_categories:
+  - 1K<n<10K
 ---
+
+# Brush Calligraphy Stroke Segmentation Dataset (BCSS) 🖌️
+
+[![GitHub](https://img.shields.io/badge/GitHub-Rvosuke%2FBCSS-181717?logo=github)](https://github.com/Rvosuke/BCSS)
+[![Paper](https://img.shields.io/badge/Paper-IET%20Image%20Processing-brightgreen)](https://doi.org/10.1049/ipr2.13255)
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+
+> This dataset card mirrors and is cross-linked with the project's GitHub repository: **[github.com/Rvosuke/BCSS](https://github.com/Rvosuke/BCSS)**. The modeling framework built on this dataset lives at **[github.com/Rvosuke/Stroke-Seg](https://github.com/Rvosuke/Stroke-Seg)**.
+
+## Introduction
+
+The **Brush Calligraphy Stroke Segmentation Dataset (BCSS)** is a resource for the task of Chinese brush-calligraphy stroke segmentation. It is derived from the Evaluated Chinese Calligraphy Copies (E3C) dataset — an aesthetic-evaluation dataset for Chinese brush calligraphy — and augmented with additional images from diverse sources to enhance diversity and support the evaluation of model generalization.
+
+Each character image is paired with a set of per-stroke binary masks, enabling **multi-label** stroke segmentation where intersecting strokes can overlap. A per-character *prior-knowledge vector* (stroke-count metadata) is also provided.
+
+## What's in this HuggingFace copy
+
+The full BCSS dataset described in the paper contains **1,322 images and 10,653 annotated strokes**. This HuggingFace release packages the segmentation-ready split that ships with the reference implementation:
+
+| Subset | Images | Notes |
+|---|---|---|
+| Training + Validation | **1,082** | Each image has 6 per-stroke masks + a prior-knowledge vector row |
+| External Testing | **130** | Held-out generalization images (input images only) |
+| **Total** | **1,212** | Segmentation-ready subset |
+
+> **Note on counts:** The paper reports 1,322 images (1,022 train/val + 300 external test) and 10,653 strokes over the *complete* collection. This packaged, segmentation-ready subset contains 1,082 train/val images and 130 external-test images. Some raw/reference material (raw instances and label source files) is hosted on the [GitHub repository](https://github.com/Rvosuke/BCSS) under `instances/` and `labels/`.
+
+All images are **400 × 400** PNGs.
+
+## Repository Layout
+
+To keep the repo efficient (thousands of small PNGs are packed into a few archives), the image/mask files are shipped as ZIP archives, while metadata is left as plain browsable files:
+
+```
+.
+├── README.md                     # This dataset card
+├── LICENSE                       # MIT License
+├── train_val_images.zip          # images/<id>.png            (1,082 files, 400x400)
+├── train_val_masks.zip           # masks/<id>/{1..6}.png       (1,082 folders x 6 masks)
+├── train_val_info.csv            # prior-knowledge vector, 1 row per train/val image
+├── external_test_images.zip      # images/<id>.png            (130 files)
+├── external_test_info.csv        # prior-knowledge vector for external test
+└── splits/
+    ├── train.txt                 # 944 ids
+    ├── val.txt                   # 98 ids
+    ├── test.txt                  # 40 ids (internal held-out)
+    └── train_test.txt            # 10 ids (small smoke-test subset)
+```
+
+After extraction, each archive expands to an `images/` or `masks/` directory:
+
+```
+train_val_images.zip   ->  images/<id>.png
+train_val_masks.zip    ->  masks/<id>/1.png ... 6.png
+external_test_images.zip -> images/<id>.png
+```
+
+### Image ↔ mask correspondence
+
+- An image `images/<id>.png` (from `train_val_images.zip`) corresponds to the mask folder `masks/<id>/` (from `train_val_masks.zip`).
+- Each mask folder holds **6 binary PNGs** (`1.png` … `6.png`), one per stroke class. During training these are thresholded (`> 150 → 1`) and stacked into a multi-channel label tensor, so overlapping/intersecting strokes are preserved as independent channels rather than a single argmax label map.
+- IDs, images, and masks are 1:1 aligned — every one of the 1,082 images has both an image file and a 6-mask folder (verified: 0 orphans on either side).
+
+### `*_info.csv` — prior-knowledge vector
+
+Each `info.csv` has one row per image, comma-separated, no header:
+
+```
+<id>,v1,v2,v3,v4,v5,v6,v7
+```
+
+- **`id`** — image identifier (matches `images/<id>.png` and `masks/<id>/`).
+- **`v1…v7`** — integer prior-knowledge / stroke-statistic values used by the model as a *Prior Knowledge Vector* to guide segmentation. Observed value ranges in this release: `v1 ∈ [0,26]`, `v2 ∈ [0,4]`, `v3 ∈ [0,10]`, `v4 ∈ [0,6]`, `v5 ∈ [0,5]`, `v6 ∈ [0,1]`, `v7 ∈ [0,3]`. `external_test_info.csv` follows the same format.
+
+### Splits
+
+The `splits/*.txt` files list image IDs (one per line) for reproducing the reference train/val/test partition used in the Stroke-Seg paper:
+
+- `train.txt` — 944 ids
+- `val.txt` — 98 ids
+- `test.txt` — 40 ids (internal held-out)
+- `train_test.txt` — 10 ids (small smoke-test subset)
+
+The external-test subset is a separate generalization benchmark (different character styles / handwriting) and is not covered by these split files.
+
+## Usage
+
+Download and extract the archives, then load images + multi-label masks with `Pillow`/`NumPy`:
+
+```python
+import os, csv, zipfile, numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+REPO = "Rvosuke/BCSS"
+
+def fetch_and_extract(filename, dest="."):
+    path = hf_hub_download(REPO, filename, repo_type="dataset")
+    with zipfile.ZipFile(path) as z:
+        z.extractall(dest)
+
+# download archives once
+fetch_and_extract("train_val_images.zip")   # -> ./images/<id>.png
+fetch_and_extract("train_val_masks.zip")    # -> ./masks/<id>/{1..6}.png
+info_path = hf_hub_download(REPO, "train_val_info.csv", repo_type="dataset")
+split_path = hf_hub_download(REPO, "splits/train.txt", repo_type="dataset")
+
+# read prior-knowledge vectors
+info = {}
+with open(info_path, errors="ignore") as f:
+    for row in csv.reader(f):
+        info[row[0]] = list(map(int, row[1:]))
+
+def load_sample(img_id, size=(400, 400)):
+    img = np.array(Image.open(f"images/{img_id}.png").convert("RGB").resize(size))
+    mask_dir = f"masks/{img_id}"
+    channels = []
+    for fn in sorted(os.listdir(mask_dir)):                 # 1.png .. 6.png
+        m = Image.open(os.path.join(mask_dir, fn)).convert("L").resize(size)
+        channels.append((np.array(m) > 150).astype(np.uint8))
+    label = np.stack(channels, axis=0)                      # (6, H, W) multi-label
+    return img, label, info[img_id]
+
+ids = [l.strip() for l in open(split_path) if l.strip()]
+img, label, prior = load_sample(ids[0])
+print(img.shape, label.shape, prior)   # (400,400,3) (6,400,400) [...]
+```
+
+## 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 dedicated external test set for measuring generalization across writing styles. The reference framework, **Stroke-Seg**, is built on DeepLab v3 and introduces a Prior Knowledge Vector, a multi-label output strategy for intersecting strokes, and a boundary-aware loss (BDLoss).
+
+## Data Sources & Composition (per paper)
+
+- **Training/Validation (1,022 in paper):** images from the **E3C** dataset.
+- **External Testing (300 in paper):**
+  - 90 E3C images with character types unseen in train/val;
+  - 113 handwritten images from the **CCSE-W** dataset;
+  - 97 images of various Chinese character styles (regular printed and brush calligraphy forms, e.g. Clerical Script).
+
+## License
+
+Released under the **MIT License** for research purposes. See `LICENSE`.
+
+## Citation
+
+If you use this dataset, code, or methods, please cite:
+
+```bibtex
+@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},
+  doi={10.1049/ipr2.13255}
+}
+```
+
+## References
+
+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.* CVPR, 3431–3440.
+4. Chen, L. C., Papandreou, G., Schroff, F., & Adam, H. (2017). *Rethinking Atrous Convolution for Semantic Image Segmentation.* arXiv:1706.05587.
+
+## Contact
+
+For inquiries about the dataset, please contact:
+
+- zeyangbai.rvo@gmail.com
+- xiebin@csu.edu.cn
+
+---
+
+*Related resources — Dataset: [github.com/Rvosuke/BCSS](https://github.com/Rvosuke/BCSS) · Model/framework: [github.com/Rvosuke/Stroke-Seg](https://github.com/Rvosuke/Stroke-Seg) · Paper: [IET Image Processing](https://doi.org/10.1049/ipr2.13255)*

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