MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character
  Recognition with over 97K Categories
Authors: name: Yuyi Zhang, name: Yongxin Shi, name: Peirong Zhang, name: Yixin Zhao, name: Zhenhua Yang, name: Lianwen Jin

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.