基于CapsNet的汉字字形表征模型

提出基于胶囊神经网络(CapsNet)的汉字字形表征模型,通过表征汉字字形中的部件实现汉字字形的表征.首先,对任一汉字字形生成所有部件类别的表征向量.然后,根据部件存在概率,利用基于欧氏距离的离群点检测,选取相应的部件表征向量.最后,由选出的部件表征向量组成该汉字的字形表征.实验表明,文中模型在仅经过部件字形训练的情况下,即可有效识别汉字部件,同时自动生成汉字字形的有效表征.

CapsNet-Based Chinese Character Font Representation Model

A CapsNet-based Chinese character font representation model is proposed to represent Chinese character font by the representation of components. Firstly, representative vectors of all categories are generated by the model. Then, a group of component representative vectors are selected by the Euclidean-distance-based outlier detection according to component probabilities. Finally, these vectors are utilized to form the Chinese character font representations. The experimental results show that the proposed model, merely trained on component fonts, is capable of identifying components of Chinese characters and automatically generating effective representation of Chinese characters.