# Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition

arXiv: https://arxiv.org/abs/1610.04057 · HTML: https://mlsanity.com/paper/1610.04057/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2016.

## Built on by (most cited first, 6 of 6)

- [SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text](https://mlsanity.com/paper/2202.11456/) (2022)
- [BViT: Broad Attention based Vision Transformer](https://mlsanity.com/paper/2202.06268/) (2022)
- [Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks](https://mlsanity.com/paper/2208.05433/) (2022)
- [A Recursive Encoding for Cuneiform Signs](https://mlsanity.com/paper/2406.17283/) (2024)
- [Sentence-level Online Handwritten Chinese Character Recognition](https://mlsanity.com/paper/2108.02561/) (2021)
- [A new database of Houma Alliance Book ancient handwritten characters and classifier fusion approach](https://mlsanity.com/paper/2207.05993/) (2022)

## Abstract (arXiv)

In this paper, we propose a novel model, named Stroke Sequence-dependent Deep Convolutional Neural Network (SSDCNN), using the stroke sequence information and eight-directional features for Online Handwritten Chinese Character Recognition (OLHCCR). On one hand, SSDCNN can learn the representation of Online Handwritten Chinese Character (OLHCC) by incorporating the natural sequence information of the strokes. On the other hand, SSDCNN can incorporate eight-directional features in a natural way. In order to train SSDCNN, we divide the process of training into two stages: 1) The training data is used to pre-train the whole architecture until the performance tends to converge. 2) Fully-connected neural network which is used to combine the stroke sequence-dependent representation with eight-directional features and softmax layer are further trained. Experiments were conducted on the OLHCCR competition tasks of ICDAR 2013. Results show that, SSDCNN can reduce the recognition error by 50% (5.13% vs 2.56%) compared to the model which only use eight-directional features. The proposed SSDCNN achieves 97.44% accuracy which reduces the recognition error by about 1.9% compared with the best submitted system on ICDAR2013 competition. These results indicate that SSDCNN can exploit the stroke sequence information to learn high-quality representation of OLHCC. It also shows that the learnt representation and the classical eight-directional features complement each other within the SSDCNN architecture.