FontRNN: Generating Large-scale Chinese Fonts via Recurrent Neural Network
FontRNN: Generating Large-scale Chinese Fonts via Recurrent Neural Network
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FontRNN：通过循环神经网络生成大型中文字体
DOI：
10.1111/cgf.13861
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发表时间：
2019
影响因子：
2.5
通讯作者：
Jianguo Xiao
中科院分区：
文献类型：
--
作者：
Shusen Tang;Zeqing Xia;Zhouhui Lian;Yingmin Tang;Jianguo Xiao
尽管深度神经网络最近取得了令人印象深刻的发展，但由于中文字形数量巨大，例如官方标准中文字符集 GB18030‐2000 由 27,533 个汉字组成，使用基于深度学习的方法生成大规模中文字体仍然是一项相当具有挑战性的任务。到目前为止，由于基于 CNN 的模型在各种应用中取得了显着的成功，大多数用于此任务的现有模型都采用卷积神经网络（CNN）来生成汉字位图图像。然而，基于 CNN 的模型更关注图像级特征，而在书写字符时通常忽略笔画顺序信息。相反，我们将汉字视为点序列（即书写轨迹），并建议通过具有单调注意机制的有效循环神经网络（RNN）模型来处理此任务，该模型可以从少至数百个训练样本中学习，然后以相同风格合成剩余数千个字符的字形。实验结果表明，我们提出的 FontRNN 可用于合成大型中文字体以及有效生成逼真的中文手写体。
Despite the recent impressive development of deep neural networks, using deep learning based methods to generate large‐scale Chinese fonts is still a rather challenging task due to the huge number of intricate Chinese glyphs, e.g., the official standard Chinese charset GB18030‐2000 consists of 27,533 Chinese characters. Until now, most existing models for this task adopt Convolutional Neural Networks (CNNs) to generate bitmap images of Chinese characters due to CNN based models' remarkable success in various applications. However, CNN based models focus more on image‐level features while usually ignore stroke order information when writing characters. Instead, we treat Chinese characters as sequences of points (i.e., writing trajectories) and propose to handle this task via an effective Recurrent Neural Network (RNN) model with monotonic attention mechanism, which can learn from as few as hundreds of training samples and then synthesize glyphs for remaining thousands of characters in the same style. Experimental results show that our proposed FontRNN can be used for synthesizing large‐scale Chinese fonts as well as generating realistic Chinese handwritings efficiently.