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HierCode: A Lightweight Hierarchical Codebook for Zero-shot Chinese Text Recognition


Core Concepts
HierCode is a novel and lightweight codebook that revolutionizes Chinese text recognition by leveraging hierarchical structures and radical prototypes, enabling zero-shot recognition with superior performance.
Abstract
Traditional one-hot encoding methods struggle with representing hierarchical radicals in Chinese characters. HierCode employs multi-hot encoding using hierarchical binary tree encoding and prototype learning. It facilitates zero-shot recognition of Out-Of-Vocabulary (OOV) characters and excels in line-level recognition tasks. Extensive experiments across diverse benchmarks showcase HierCode's superiority in both conventional and zero-shot Chinese text recognition.
Stats
The latest Chinese standard, GB18030-20221, contains 87,887 categories. In systems like PP-OCR, the classification layer for 20,000 character categories can constitute over 60% of the model’s total parameters.
Quotes
"HierCode not only addresses the challenge of zero-shot character recognition but also proves effective in line-level recognition tasks." "Extensive experiments demonstrate HierCode's superior performance over traditional one-hot encoding methods."

Key Insights Distilled From

by Yuyi Zhang,Y... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13761.pdf
HierCode

Deeper Inquiries

How can HierCode be adapted to handle complex backgrounds in text recognition?

HierCode can be adapted to handle complex backgrounds in text recognition by incorporating advanced image processing techniques. One approach could involve pre-processing the input images to remove noise, enhance contrast, and segment text regions effectively. Additionally, integrating HierCode with attention mechanisms or contextual embeddings can help the model focus on relevant parts of the text despite complex backgrounds. By training the model on diverse datasets with varying background complexities, HierCode can learn robust features that enable it to perform well even in challenging scenarios.

What are the limitations of radical-based methods like HierCode?

One limitation of radical-based methods like HierCode is their reliance on accurate decomposition of characters into radicals. If a character has an unconventional structure or contains rare radicals, it may not be accurately represented using this method. Additionally, similar radicals shared among different characters can lead to confusion during recognition tasks. Another limitation is that radical-based methods may struggle with characters that do not have distinct structural components or consist of multiple intricate parts that are hard to decompose effectively.

How can the concept of HierCode be applied to other languages or scripts beyond Chinese characters?

The concept of HierCode can be applied to other languages or scripts beyond Chinese characters by adapting the hierarchical encoding and prototype learning principles for those specific scripts. For each language, a unique set of structures and components would need to be identified and encoded hierarchically. The radical prototypes used in Chinese character recognition could be replaced with relevant linguistic units for other languages. Additionally, considering variations in script complexity and character composition across different languages, customization would be essential when applying HierCode methodology outside Chinese script recognition. By tailoring the hierarchical representation process according to the characteristics of each language's script system, Hiercode could potentially achieve superior performance in recognizing texts from various linguistic backgrounds.
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