# Drawing and Recognizing Chinese Characters with Recurrent Neural Network

arXiv: https://arxiv.org/abs/1606.06539 · HTML: https://mlsanity.com/paper/1606.06539/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2016.

## Built on by (most cited first, 26 of 26)

- [Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks](https://mlsanity.com/paper/2103.06982/) (2021)
- [Toward Understanding WordArt: Corner-Guided Transformer for Scene Text Recognition](https://mlsanity.com/paper/2208.00438/) (2022)
- [Content and Style Aware Generation of Text-line Images for Handwriting Recognition](https://mlsanity.com/paper/2204.05539/) (2022)
- [Disentangling Writer and Character Styles for Handwriting Generation](https://mlsanity.com/paper/2303.14736/) (2023)
- [SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text](https://mlsanity.com/paper/2202.11456/) (2022)
- [DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning](https://mlsanity.com/paper/2110.06688/) (2021)
- [Recognition of Handwritten Chinese Text by Segmentation: A Segment-annotation-free Approach](https://mlsanity.com/paper/2207.14801/) (2022)
- [Complex Handwriting Trajectory Recovery: Evaluation Metrics and Algorithm](https://mlsanity.com/paper/2210.15879/) (2022)
- [PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition](https://mlsanity.com/paper/2207.14807/) (2022)
- [Sequence-based Dynamic Handwriting Analysis for Parkinson's Disease Detection with One-dimensional Convolutions and BiGRUs](https://mlsanity.com/paper/2101.09461/) (2021)
- [Decoupling Layout from Glyph in Online Chinese Handwriting Generation](https://mlsanity.com/paper/2410.02309/) (2024)
- [SVG Vector Font Generation for Chinese Characters with Transformer](https://mlsanity.com/paper/2206.10329/) (2022)
- [FontTransformer: Few-shot High-resolution Chinese Glyph Image Synthesis via Stacked Transformers](https://mlsanity.com/paper/2210.06301/) (2022)
- [DeepCalliFont: Few-shot Chinese Calligraphy Font Synthesis by Integrating Dual-modality Generative Models](https://mlsanity.com/paper/2312.10314/) (2023)
- [Finger in Camera Speaks Everything: Unconstrained Air-Writing for Real-World](https://mlsanity.com/paper/2412.19537/) (2024)
- [InkFM: A Foundational Model for Full-Page Online Handwritten Note Understanding](https://mlsanity.com/paper/2503.23081/) (2025)
- [Writing in The Air: Unconstrained Text Recognition from Finger Movement Using Spatio-Temporal Convolution](https://mlsanity.com/paper/2104.09021/) (2021)
- [Generation Drawing/Grinding Trajectoy Based on Hierarchical CVAE](https://mlsanity.com/paper/2111.10954/) (2021)
- [DiffInk: Glyph- and Style-Aware Latent Diffusion Transformer for Text to Online Handwriting Generation](https://mlsanity.com/paper/2509.23624/) (2025)
- [GSSF: A Generative Sequence Similarity Function based on a Seq2Seq model for clustering online handwritten mathematical answers](https://mlsanity.com/paper/2105.10159/) (2021)
- [Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data](https://mlsanity.com/paper/2211.16653/) (2022)
- [W-Net: One-Shot Arbitrary-Style Chinese Character Generation with Deep Neural Networks](https://mlsanity.com/paper/2406.06122/) (2024)
- [DNA: Dual-branch Network with Adaptation for Open-Set Online Handwriting Generation](https://mlsanity.com/paper/2511.22064/) (2025)
- [Rotation-free Online Handwritten Character Recognition Using Linear Recurrent Units](https://mlsanity.com/paper/2602.01533/) (2026)
- [ScribeTokens: Fixed-Vocabulary Tokenization of Digital Ink](https://mlsanity.com/paper/2603.02805/) (2026)
- [BoundInk: Boundary-Aware Online Handwriting Generation](https://mlsanity.com/paper/2604.02103/) (2026)

## Abstract (arXiv)

Recent deep learning based approaches have achieved great success on handwriting recognition. Chinese characters are among the most widely adopted writing systems in the world. Previous research has mainly focused on recognizing handwritten Chinese characters. However, recognition is only one aspect for understanding a language, another challenging and interesting task is to teach a machine to automatically write (pictographic) Chinese characters. In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese characters. To recognize Chinese characters, previous methods usually adopt the convolutional neural network (CNN) models which require transforming the online handwriting trajectory into image-like representations. Instead, our RNN based approach is an end-to-end system which directly deals with the sequential structure and does not require any domain-specific knowledge. With the RNN system (combining an LSTM and GRU), state-of-the-art performance can be achieved on the ICDAR-2013 competition database. Furthermore, under the RNN framework, a conditional generative model with character embedding is proposed for automatically drawing recognizable Chinese characters. The generated characters (in vector format) are human-readable and also can be recognized by the discriminative RNN model with high accuracy. Experimental results verify the effectiveness of using RNNs as both generative and discriminative models for the tasks of drawing and recognizing Chinese characters.