Abstract:

Handwritten Chinese Character Generation（HCCG） is an important research direction in machine learning. In the past two decades the studies of handwritten Chinese character generation can be roughly divided into two stages：the research in the early stage mainly used the explicit characteristics of Chinese characters, such as the structure and strokes, to realize the decomposition of Chinese characters, and then realized the generation of Chinese characters through algorithms. This type of method has relatively high requirements for the decomposition accuracy of Chinese characters dataset, which limits the wide application of this type of method. The current research on Chinese character generation mainly uses deep neural networks to extract the implicit features of Chinese characters, to generate higher-quality Chinese characters and overcome problems such as insufficient data sets in the early research stage. The main purpose of this article is to conduct a comprehensive and systematic review of the existing research on Chinese character generation.

Key words: handwritten Chinese character generation, shallow learning, deep learning

摘要：

手写汉字生成是机器学习中一个重要的研究方向。近二十年来，针对手写汉字生成的研究大体可分为两个阶段：早期主要利用汉字的显式特征如结构和笔画等实现对汉字的分解，再通过算法实现汉字的生成。该类方法对汉字的分解准确度及数据集的精度要求较高，限制了该类方法的广泛应用。现阶段的汉字生成研究主要借助于深度神经网络来实现对汉字隐式特征的提取，从而生成更高质量的汉字并克服早期研究阶段数据集不足等问题。主要目的是对已有汉字生成研究进行全面系统的综述。

关键词: 手写汉字生成, 浅层学习, 深度学习