MegaHan97K：面向超大类中文字符识别的大规模数据集，涵盖超过97,000个类别

MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character Recognition with over 97K Categories

June 5, 2025

作者: Yuyi Zhang, Yongxin Shi, Peirong Zhang, Yixin Zhao, Zhenhua Yang, Lianwen Jin

cs.AI

摘要

作为中华语言与文化的基石，汉字涵盖了极其广泛且不断扩展的类别，最新的GB18030-2022标准收录了87,887个字符类别。准确识别如此庞大的字符集，即所谓的超大类识别，对于文化遗产保护及数字化应用而言，既是一项艰巨又至关重要的挑战。尽管光学字符识别（OCR）技术已取得显著进展，但由于缺乏全面的数据集，超大类识别领域仍未被充分探索，现有最大数据集仅包含16,151个类别。为填补这一关键空白，我们推出了MegaHan97K，这是一个超大类、大规模的数据集，前所未有地覆盖了97,455个汉字类别。我们的工作贡献主要体现在三个方面：（1）MegaHan97K是首个全面支持最新GB18030-2022标准的数据集，提供的类别数量至少是现有数据集的六倍；（2）通过其三个独特子集——手写体、历史文献及合成子集，有效解决了长尾分布问题，为所有类别提供了均衡的样本；（3）全面的基准测试实验揭示了超大类场景下的新挑战，包括存储需求增加、形态相似字符识别及零样本学习难题，同时也为未来研究开辟了广阔机遇。据我们所知，MegaHan97K不仅在OCR领域，甚至可能在更广泛的模式识别领域内，都是类别数量最为庞大的数据集。该数据集可通过https://github.com/SCUT-DLVCLab/MegaHan97K获取。

English

Foundational to the Chinese language and culture, Chinese characters encompass extraordinarily extensive and ever-expanding categories, with the latest Chinese GB18030-2022 standard containing 87,887 categories. The accurate recognition of this vast number of characters, termed mega-category recognition, presents a formidable yet crucial challenge for cultural heritage preservation and digital applications. Despite significant advances in Optical Character Recognition (OCR), mega-category recognition remains unexplored due to the absence of comprehensive datasets, with the largest existing dataset containing merely 16,151 categories. To bridge this critical gap, we introduce MegaHan97K, a mega-category, large-scale dataset covering an unprecedented 97,455 categories of Chinese characters. Our work offers three major contributions: (1) MegaHan97K is the first dataset to fully support the latest GB18030-2022 standard, providing at least six times more categories than existing datasets; (2) It effectively addresses the long-tail distribution problem by providing balanced samples across all categories through its three distinct subsets: handwritten, historical and synthetic subsets; (3) Comprehensive benchmarking experiments reveal new challenges in mega-category scenarios, including increased storage demands, morphologically similar character recognition, and zero-shot learning difficulties, while also unlocking substantial opportunities for future research. To the best of our knowledge, the MetaHan97K is likely the dataset with the largest classes not only in the field of OCR but may also in the broader domain of pattern recognition. The dataset is available at https://github.com/SCUT-DLVCLab/MegaHan97K.