# SAN: Structure-Aware Network for Complex and Long-tailed Chinese Text Recognition

arXiv: https://arxiv.org/abs/2411.06381 · HTML: https://mlsanity.com/paper/2411.06381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 10 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition](https://mlsanity.com/paper/2103.06495/) (2021)
- [What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis](https://mlsanity.com/paper/1904.01906/) (2019)
- [Towards Accurate Scene Text Recognition with Semantic Reasoning Networks](https://mlsanity.com/paper/2003.12294/) (2020)
- [Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition](https://mlsanity.com/paper/1811.00751/) (2018)
- [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://mlsanity.com/paper/2005.10977/) (2020)
- [Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study](https://mlsanity.com/paper/2112.15093/) (2021)
- [Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition](https://mlsanity.com/paper/2106.11613/) (2021)
- [DenseRAN for Offline Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1808.04134/) (2018)
- [Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification](https://mlsanity.com/paper/1309.0261/) (2013)
- [Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1702.07975/) (2017)
- [Writer-Aware CNN for Parsimonious HMM-Based Offline Handwritten Chinese Text Recognition](https://mlsanity.com/paper/1812.09809/) (2018)
- [Template-Instance Loss for Offline Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1910.05545/) (2019)
- [Maximum Entropy Regularization and Chinese Text Recognition](https://mlsanity.com/paper/2007.04651/) (2020)
- [Radical analysis network for zero-shot learning in printed Chinese character recognition](https://mlsanity.com/paper/1711.01889/) (2017)
- [Open Set Chinese Character Recognition using Multi-typed Attributes](https://mlsanity.com/paper/1808.08993/) (2018)

## Built on by (most cited first, 1 of 1)

- [Multi-Modal Character Localization and Extraction for Chinese Text Recognition](https://mlsanity.com/paper/2603.13886/) (2026)

## Abstract (arXiv)

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.