# Chinese Character Recognition with Radical-Structured Stroke Trees

arXiv: https://arxiv.org/abs/2211.13518 · HTML: https://mlsanity.com/paper/2211.13518/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 24 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [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)
- [Online and Offline Handwritten Chinese Character Recognition: A Comprehensive Study and New Benchmark](https://mlsanity.com/paper/1606.05763/) (2016)
- [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)
- [High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps](https://mlsanity.com/paper/1505.04925/) (2015)
- [Template-Instance Loss for Offline Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1910.05545/) (2019)
- [Building Efficient CNN Architecture for Offline Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1804.01259/) (2018)
- [Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition](https://mlsanity.com/paper/2207.08191/) (2022)

## Built on by (most cited first, 12 of 12)

- [Chinese Text Recognition with A Pre-Trained CLIP-Like Model Through Image-IDS Aligning](https://mlsanity.com/paper/2309.01083/) (2023)
- [DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions](https://mlsanity.com/paper/2309.04682/) (2023)
- [Ancient Script Image Recognition and Processing: A Review](https://mlsanity.com/paper/2506.19208/) (2025)
- [Orientation-Independent Chinese Text Recognition in Scene Images](https://mlsanity.com/paper/2309.01081/) (2023)
- [Interpretable Oracle Bone Script Decipherment through Radical and Pictographic Analysis with LVLMs](https://mlsanity.com/paper/2508.10113/) (2025)
- [Improving Chinese Character Representation with Formation Tree](https://mlsanity.com/paper/2404.12693/) (2024)
- [CoLa: Chinese Character Decomposition with Compositional Latent Components](https://mlsanity.com/paper/2506.03798/) (2025)
- [Zero-Shot Chinese Character Recognition with Hierarchical Multi-Granularity Image-Text Aligning](https://mlsanity.com/paper/2505.24837/) (2025)
- [Computer-Aided Multi-Stroke Character Simplification by Stroke Removal](https://mlsanity.com/paper/2506.23106/) (2025)
- [Interpretable Mnemonic Generation for Kanji Learning via Expectation-Maximization](https://mlsanity.com/paper/2507.05137/) (2025)
- [Entropy-Aware Structural Alignment for Zero-Shot Handwritten Chinese Character Recognition](https://mlsanity.com/paper/2602.03913/) (2026)
- [Zero-Shot Chinese Character Recognition via Global-Local Dual-Branch Alignment and Hierarchical Inference](https://mlsanity.com/paper/2605.08814/) (2026)

## Abstract (arXiv)

The flourishing blossom of deep learning has witnessed the rapid development of Chinese character recognition. However, it remains a great challenge that the characters for testing may have different distributions from those of the training dataset. Existing methods based on a single-level representation (character-level, radical-level, or stroke-level) may be either too sensitive to distribution changes (e.g., induced by blurring, occlusion, and zero-shot problems) or too tolerant to one-to-many ambiguities. In this paper, we represent each Chinese character as a stroke tree, which is organized according to its radical structures, to fully exploit the merits of both radical and stroke levels in a decent way. We propose a two-stage decomposition framework, where a Feature-to-Radical Decoder perceives radical structures and radical regions, and a Radical-to-Stroke Decoder further predicts the stroke sequences according to the features of radical regions. The generated radical structures and stroke sequences are encoded as a Radical-Structured Stroke Tree (RSST), which is fed to a Tree-to-Character Translator based on the proposed Weighted Edit Distance to match the closest candidate character in the RSST lexicon. Our extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art single-level methods by increasing margins as the distribution difference becomes more severe in the blurring, occlusion, and zero-shot scenarios, which indeed validates the robustness of the proposed method.