SAN: Structure-Aware Network for Complex and Long-tailed Chinese Text Recognition
This work addresses recognition challenges for complex and rare Chinese characters, representing an incremental improvement in domain-specific text recognition.
The paper tackles the problem of Chinese text recognition for complex glyphs and tail classes by proposing a structure-aware network that uses hierarchical composition information, resulting in significant performance improvements for these challenging characters.
In text recognition, complex glyphs and tail classes have always been factors affecting model performance. Specifically for Chinese text recognition, the lack of shape-awareness can lead to confusion among close complex characters. Since such characters are often tail classes that appear less frequently in the training-set, making it harder for the model to capture its shape information. Hence in this work, we propose a structure-aware network utilizing the hierarchical composition information to improve the recognition performance of complex characters. Implementation-wise, we first propose an auxiliary radical branch and integrate it into the base recognition network as a regularization term, which distills hierarchical composition information into the feature extractor. A Tree-Similarity-based weighting mechanism is then proposed to further utilize the depth information in the hierarchical representation. Experiments demonstrate that the proposed approach can significantly improve the performances of complex characters and tail characters, yielding a better overall performance. Code is available at https://github.com/Levi-ZJY/SAN.