- 學位論文
書法風格的手寫字轉換：一種基於生成對抗網路的方法
Handwriting to Calligraphy Style Transformation: A Generative Adversarial Network Approach
摘要
中國書法是使用毛筆書寫文字的一門藝術，不同風格的文字具有不同的形狀與細節，展現出來的美感也不同。為了推廣書法技藝，我們在本研究提出一個字體轉換的方法。不像現有方法幾乎只針對特定印刷字體和書法字做轉換。我們的研究也包含將非專家寫的手寫字作為輸入並轉換成書法風格。我們的研究是基於生成對抗網路 (Generative Adversarial Network, GAN)，讓電腦讀取大量手寫字與其相應的書法字，建構出一個能將手寫字風格轉換為書法字風格的人工智慧系統。我們使用基於U-Net的生成器，還使用了風格標籤的嵌入控制模型生成的文字風格，並且將文字的骨架資訊運用於我們的模型中，透過加入骨架資訊可以使模型更穩定的將文字重建出來。最後，我們將我們的方法與其他目前最先進的字體生成方法進行比較，證明我們的方法優於過去的書法字風格轉換方法。
並列摘要
Chinese calligraphy is an art of writing characters with a brush. Different styles of characters have different shapes and details. These characters show different aesthetics. To promote calligraphy skills, we propose a method to transfer handwritten characters into calligraphy fonts in this study. Unlike existing methods, which almost only research on specific printed fonts and calligraphy characters. Our research also includes handwritten characters written by non-experts as input and converted into calligraphic styles. Our research is based on Generative Adversarial Network (GAN). It allows computers to read a large number of handwritten characters and their corresponding calligraphy characters. Then the artificial intelligence model can transfer handwritten characters into calligraphic styles. The generator in our method is based on U-Net. We use the embedding of style labels to control the style generated by the model and apply the skeleton information of characters to our model. By adding skeleton information, the model can generate character more stable. Finally, we compare our method with other state-of-the-art font generation methods and demonstrate that our method outperforms past calligraphic character style transfer methods.