Sentence-level Online Handwritten Chinese Character Recognition

• URL: http://arxiv.org/abs/2108.02561v1
• Date: Sun, 4 Jul 2021 14:26:06 GMT
• Title: Sentence-level Online Handwritten Chinese Character Recognition
• Authors: Yunxin Li, Qian Yang, Qingcai Chen, Lin Ma, Baotian Hu, Xiaolong Wang, Yuxin Ding
• Abstract summary: Single online handwritten Chinese character recognition(single OLHCCR) has achieved prominent performance. In real application scenarios, users always write multiple Chinese characters to form one complete sentence. We propose a simple and straightforward end-to-end network, namely vanilla compositional network(VCN) to tackle the sentence-level OLHCCR. We also propose a novel deep spatial-temporal fusion network(DSTFN) to improve the robustness of sentence-level OLHCCR.
• Score: 36.57575120082676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single online handwritten Chinese character recognition~(single OLHCCR) has achieved prominent performance. However, in real application scenarios, users always write multiple Chinese characters to form one complete sentence and the contextual information within these characters holds the significant potential to improve the accuracy, robustness and efficiency of sentence-level OLHCCR. In this work, we first propose a simple and straightforward end-to-end network, namely vanilla compositional network~(VCN) to tackle the sentence-level OLHCCR. It couples convolutional neural network with sequence modeling architecture to exploit the handwritten character's previous contextual information. Although VCN performs much better than the state-of-the-art single OLHCCR model, it exposes high fragility when confronting with not well written characters such as sloppy writing, missing or broken strokes. To improve the robustness of sentence-level OLHCCR, we further propose a novel deep spatial-temporal fusion network~(DSTFN). It utilizes a pre-trained autoregresssive framework as the backbone component, which projects each Chinese character into word embeddings, and integrates the spatial glyph features of handwritten characters and their contextual information multiple times at multi-layer fusion module. We also construct a large-scale sentence-level handwriting dataset, named as CSOHD to evaluate models. Extensive experiment results demonstrate that DSTFN achieves the state-of-the-art performance, which presents strong robustness compared with VCN and exiting single OLHCCR models. The in-depth empirical analysis and case studies indicate that DSTFN can significantly improve the efficiency of handwriting input, with the handwritten Chinese character with incomplete strokes being recognized precisely.

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