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Unsupervised Structure-Texture Separation Network for Oracle Character Recognition.

Mei Wang, Weihong Deng, Cheng-Lin Liu

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|April 14, 2022

Summary

This summary is machine-generated.

This study introduces a novel Structure-Texture Separation Network (STSN) for recognizing ancient Chinese oracle bone script. The method effectively adapts knowledge from handprinted characters to improve recognition of rare scanned data.

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Area of Science:

Archaeology
Philology
Computer Vision
Machine Learning

Background:

Oracle bone script, the earliest Chinese writing system, is crucial for historical research.
Automatic recognition of scanned oracle characters is challenging due to data scarcity and expert annotation limitations.
Unsupervised domain adaptation offers a potential solution for knowledge transfer from abundant handprinted data to scarce scanned data.

Purpose of the Study:

To develop an effective unsupervised domain adaptation method for recognizing scanned oracle bone script.
To address the challenges posed by data scarcity and noise in real-world scanned oracle data.
To improve the accuracy of automatic oracle character recognition by leveraging readily available handprinted data.

Main Methods:

Proposing a Structure-Texture Separation Network (STSN) for end-to-end learning.
Disentangling features into structure (glyph) and texture (noise) components using generative models.
Aligning handprinted and scanned data in structure feature space and swapping textures for domain adaptation.

Main Results:

The STSN framework successfully disentangles structure and texture components.
Alignment in structure feature space mitigates the impact of noise in scanned data.
The proposed method significantly outperforms existing domain adaptation techniques on the Oracle-241 dataset.
Recognition performance on scanned oracle characters, even those heavily degraded, is substantially improved.

Conclusions:

STSN provides an effective approach for unsupervised domain adaptation in recognizing historical scripts.
The structure-texture separation mechanism enhances feature discriminability and robustness to noise.
This research advances the automatic recognition of ancient artifacts, aiding archaeological and philological studies.