MetaScript：基于生成对抗网络的少样本手写中文内容生成
MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial Networks
AI总结：
提出MetaScript系统，利用少样本学习和生成对抗网络，从少量风格参考生成保留个人手写风格的中文内容，在识别准确率等指标上表现优异。
摘要：
在这项工作中，我们提出了MetaScript，一种新颖的中文内容生成系统，旨在解决个人手写风格在汉字数字表示中逐渐消失的问题。我们的方法利用少样本学习的力量来生成汉字，这些汉字不仅保留了个体独特的手写风格，而且保持了数字打字的高效性。MetaScript在多样化的手写风格数据集上训练，擅长从极少的风格参考和标准字体中产生高质量的文体模仿。我们的工作为数字排版在保持书面交流中个人触感方面的挑战提供了一种实用的解决方案，特别是在中文书写的背景下。值得注意的是，我们的系统在各种评估中表现出优越的性能，包括识别准确率、初始分数和弗雷歇初始距离。同时，我们模型的训练条件易于满足，并有助于推广到实际应用中。
英文摘要：
In this work, we propose MetaScript, a novel Chinese content generation system designed to address the diminishing presence of personal handwriting styles in the digital representation of Chinese characters. Our approach harnesses the power of few-shot learning to generate Chinese characters that not only retain the individual's unique handwriting style but also maintain the efficiency of digital typing. Trained on a diverse dataset of handwritten styles, MetaScript is adept at producing high-quality stylistic imitations from minimal style references and standard fonts. Our work demonstrates a practical solution to the challenges of digital typography in preserving the personal touch in written communication, particularly in the context of Chinese script. Notably, our system has demonstrated superior performance in various evaluations, including recognition accuracy, inception score, and Frechet inception distance. At the same time, the training conditions of our model are easy to meet and facilitate generalization to real applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
