论文标题
字符作为图表：通过空间图卷积网络识别在线手写的汉字
Characters as Graphs: Recognizing Online Handwritten Chinese Characters via Spatial Graph Convolutional Network
论文作者
论文摘要
中文是世界上使用最广泛的语言之一，但是在线手写的中文识别（OLHCCR）仍然具有挑战性。为了识别汉字，一个流行的选择是在提取的特征图像上采用2D卷积神经网络（2D-CNN），另一个是在时间序列特征上使用经常性神经网络（RNN）或1D-CNN。我们在这里建议将字符视为静态图像或时间轨迹，而是将字符表示为几何图形，同时保留空间结构和时间顺序。因此，我们提出了一个新型的空间图卷积网络（SGCN），以首次有效地对这些角色图进行分类。具体而言，我们的SGCN通过空间图卷积结合了本地邻域信息，并进一步了解了具有层次残留结构的全球形状属性。 IAHCC-UCAS2016，ICDAR-2013和UNIPEN数据集的实验表明，SGCN可以通过最新的角色识别方法实现可比的识别性能。
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.
