论文标题
文本和样式有条件的gan，用于生成离线手写线条
Text and Style Conditioned GAN for Generation of Offline Handwriting Lines
论文作者
论文摘要
本文提出了一个用于生成以任意文本和潜在样式向量为条件的手写线的图像。与先前产生冲程点或单词图像的工作不同，该模型生成了整个离线手写线。该模型通过使用样式向量来确定字符宽度，从而产生可变大小的图像。发电机网络接受了GAN和自动编码器技术的培训，以学习样式，并使用预先训练的手写识别网络来诱导可读性。使用人类评估者的一项研究表明，该模型产生了似乎由人写的图像。训练后，编码器网络可以从图像中提取样式向量，从而允许以类似样式生成的图像，但具有任意文本。
This paper presents a GAN for generating images of handwritten lines conditioned on arbitrary text and latent style vectors. Unlike prior work, which produce stroke points or single-word images, this model generates entire lines of offline handwriting. The model produces variable-sized images by using style vectors to determine character widths. A generator network is trained with GAN and autoencoder techniques to learn style, and uses a pre-trained handwriting recognition network to induce legibility. A study using human evaluators demonstrates that the model produces images that appear to be written by a human. After training, the encoder network can extract a style vector from an image, allowing images in a similar style to be generated, but with arbitrary text.
