- 學位論文
基於生成對抗網路GAN模型之書法字體生成系統
Calligraphy Character Generation System Based on the Generative Adversarial Networks Model
摘要
書法是我國文字書寫的藝術表現，有豐富的形狀和變化。本論文嘗試使用生成對抗網路來模擬生成歷代名家的書法字體。我們以書法名家的書法字體影像為學習的訓練資料集。本研究中，我們以Zi2Zi的方法為基礎，實作書法字體風格轉換的生成對抗網路模型，並探討其他研究的一些方法對模型產生的影響，嘗試得出最佳化的深度學習模型。我們探討加入U-Net、類別內嵌(Category Embedding)、和HAN等方法，以及訓練資料集大小對模型造成的影響。在最後實驗中，我們比較了pix2pix模型、Zi2Zi模型、HAN模型、和HAN-Zi2Zi模型。經過實驗後發現，加入U-Net和Category Embedding都對模型的成果有所幫助，而使用越多字體進行訓練會有越好的成效。另外，HAN-Zi2Zi效果最好。
並列摘要
Calligraphy is the artistic expression of Chinese character writing, with rich shapes and variations. This research attempts to use the Generative Adversarial Network to generate the calligraphy characters of famous calligraphers. We use calligraphy font images of famous calligraphy masters as training materials for learning. In this research, based on the Zi2Zi method, we implemented a generational confrontation network model for calligraphic font style conversion, and explored the impact of some other research methods on the model, and tried to arrive at an optimized deep learning model. We discuss methods such as adding U-Net, Category Embedding, and HAN, as well as the impact of the size of the training data set on the model. In the final experiment, we compared the pix2pix model, Zi2Zi model, HAN model, and HAN-Zi2Zi model. After experimentation, it is found that adding U-Net and Category Embedding are helpful to the results of the model, and the more fonts are used for training, the better the results will be. In addition, HAN-Zi2Zi works best.