  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.
