GAS-Net: Generative Artistic Style Neural Networks for Fonts

• URL: http://arxiv.org/abs/2212.02886v1
• Date: Tue, 6 Dec 2022 11:23:16 GMT
• Title: GAS-Net: Generative Artistic Style Neural Networks for Fonts
• Authors: Haoyang He, Xin Jin, Angela Chen
• Abstract summary: This project aims to develop a few-shot cross-lingual font generator based on AGIS-Net. Our approaches include redesigning the encoder and the loss function. We will validate our method on multiple languages and datasets mentioned.
• Score: 8.569974263629218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating new fonts is a time-consuming and labor-intensive, especially in a language with a huge amount of characters like Chinese. Various deep learning models have demonstrated the ability to efficiently generate new fonts with a few reference characters of that style. This project aims to develop a few-shot cross-lingual font generator based on AGIS-Net and improve the performance metrics mentioned. Our approaches include redesigning the encoder and the loss function. We will validate our method on multiple languages and datasets mentioned.

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