MCCD

KO | EN

gitlite — search

#javascript #react #llm #golang #android #machine-learning #rust #awesome #cli #kubernetes #vue #pytorch #security #openai

(★ 40)

[ICDAR 2025] The official GitHub page of "MCCD: A Multi-Attribute Chinese Calligraphy Character Dataset Annotated with Script Styles, Dynasties, and Calligraphers"

MCCD 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# <div align=center>MCCD: A Multi-Attribute Chinese Calligraphy Character Dataset Annotated with Script Styles, Dynasties, and Calligraphers</div>
<div align="center">

[![SCUT DLVC Lab](https://img.shields.io/badge/SCUT-DLVC_Lab-327FE6?logo=Academia&logoColor=white&style=plastic)](http://dlvc-lab.net/lianwen/)     ![ICDAR2025](https://img.shields.io/badge/2025-ICDAR?style=plastic&label=ICDAR&labelColor=grey&color=%2300BFFF) [![arxiv preprint](https://img.shields.io/badge/2507.06948-arxiv?style=plastic&label=arxiv&labelColor=grey&color=%23DC143C&link=https%3A%2F%2Farxiv.org%2Fabs%2F2507.06948)](https://arxiv.org/abs/2507.06948) [![Code](https://img.shields.io/badge/MCCD-Code?style=plastic&label=Code&labelColor=grey&color=%23DB7093)](https://github.com/SCUT-DLVCLab/MCCD)

</div>

![overview](https://github.com/SCUT-DLVCLab/MCCD/blob/main/picture/overview.png)

## ✨ introduction
- We introduce **Multi-Attribute Chinese Calligraphy Character Dataset (MCCD)**, an isolated Chinese character dataset with rich annotations including character, script style, dynasty, and calligrapher.
- **Extensive Multi-Attribute Collection:** MCCD dataset presents a meticulously curated collection of nearly 330,000 calligraphic character images, ensuring a comprehensive diversity of annotation categories for all characters and their attributes (style, dynasty, and calligrapher).
- **Multi-Attribute Subset Construction:** MCCD contains labels for 7,765 categories of characters, in addition to which three additional subsets are extracted from the dataset according to the attribute annotations for each character, including 10 styles of calligraphy, 15 major historical dynasties and 142 famous calligraphers, with the aim of optimizing task-specific utilization of the attribute information.
- **Benchmark Establishment:** We established benchmark performance metrics for single-task recognition (character and each attribute independently) and multi-task recognition (character combined with other attributes simultaneously) experiments using MCCD and all its subsets.

## 🔗 Download
✅ **Status:** Released

✅ **Dataset link:** [Baiduyun:8x7d]( https://pan.baidu.com/s/1qkM_1gizNRBONEY47nw50g?pwd=8x7d) / [OneDrive](https://1drv.ms/u/c/d3b0ec8fe3491f94/EXtpGohdU55Io8CUtbfjShkBocjgdcDl4y0j-_gJRN-LNQ?e=ShhRhh)

✅ **Data format:** PNG / lmdb

## 🛠️ Usage
- Clone this repo:
```bash
git clone https://github.com/SCUT-DLVCLab/MCCD.git
```
- The **data_loader** folder contains **read files** for single-attribute labeled lmdb as well as 2-attribute labeled and 4-attribute labeled lmdb data.

|Read File                 |Corresponding Dataset                                     
|--------------------------|---------------------------------------------|
|lmdb_dataset.py           |`MCCD-Character/ Style/ Dynasty/Calligrapher`            
|2task_MTL_lmdb_dataset.py |`dual_task`                                  |
|4task_MTL_lmdb_dataset.py |`four_task`|

❗**Note:**
- The MCCD dataset can only be used for non-commercial research purposes. Scholars or organizations who wish to use the MCCD dataset can apply through our online platform:  👉 [Apply Here](http://121.41.49.212:9000/)
- We will give you the decompression password after your application has been received and approved.
- All users must follow all use conditions; otherwise, the authorization will be revoked.

## 📧 Contact
☺️ If you have any questions, please feel free to contact [Yixin Zhao](https://github.com/Christinazyx) at yixin_zhao01@126.com.

## 🔐License
MCCD should be used and distributed under [Creative Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License](https://creativecommons.org/licenses/by-nc-nd/4.0/) for non-commercial research purposes.

## ©️ Copyright
- This repository can only be used for non-commercial research purposes.
- Copyright 2025, [Deep Learning and Vision Computing Lab (DLVC-Lab)](http://www.dlvc-lab.net/), South China University of Technology.

MCCD

MCCD 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

MCCD

MCCD 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README