  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.
