  Chinese is one of the most widely used languages in the world, yet online
handwritten Chinese character recognition (OLHCCR) remains challenging. To
recognize Chinese characters, one popular choice is to adopt the 2D
convolutional neural network (2D-CNN) on the extracted feature images, and
another one is to employ the recurrent neural network (RNN) or 1D-CNN on the
time-series features. Instead of viewing characters as either static images or
temporal trajectories, here we propose to represent characters as geometric
graphs, retaining both spatial structures and temporal orders. Accordingly, we
propose a novel spatial graph convolution network (SGCN) to effectively
classify those character graphs for the first time. Specifically, our SGCN
incorporates the local neighbourhood information via spatial graph convolutions
and further learns the global shape properties with a hierarchical residual
structure. Experiments on IAHCC-UCAS2016, ICDAR-2013, and UNIPEN datasets
demonstrate that the SGCN can achieve comparable recognition performance with
the state-of-the-art methods for character recognition.
