HierCode: A Lightweight Hierarchical Codebook for Zero-shot Chinese Text
  Recognition
Yuyi Zhang Yuanzhi Zhu Dezhi Peng Peirong Zhang Zhenhua Yang Zhibo Yang Cong Yao Lianwen Jin
Text recognition, especially for complex scripts like Chinese, faces unique
challenges due to its intricate character structures and vast vocabulary.
Traditional one-hot encoding methods struggle with the representation of
hierarchical radicals, recognition of Out-Of-Vocabulary (OOV) characters, and
on-device deployment due to their computational intensity. To address these
challenges, we propose HierCode, a novel and lightweight codebook that exploits
the innate hierarchical nature of Chinese characters. HierCode employs a
multi-hot encoding strategy, leveraging hierarchical binary tree encoding and
prototype learning to create distinctive, informative representations for each
character. This approach not only facilitates zero-shot recognition of OOV
characters by utilizing shared radicals and structures but also excels in
line-level recognition tasks by computing similarity with visual features, a
notable advantage over existing methods. Extensive experiments across diverse
benchmarks, including handwritten, scene, document, web, and ancient text, have
showcased HierCode's superiority for both conventional and zero-shot Chinese
character or text recognition, exhibiting state-of-the-art performance with
significantly fewer parameters and fast inference speed.