DOI: 10.6084/m9.figshare.25054619
title: Metadata supporting data files in the published article: &lt;<b>EdgeStyleGAN: An Optimization Approach for Chinese Character Font Generation Based on Font Contours&gt;</b>

description: Figure posted on 2024-01-24, 12: 30 authored by Chang Liu, Qiange Wan, Zhi Zheng, Yue Zhao.Our paper proposes a Chinese character font generation method based on font contour information (EdgeStyleGAN). In order to verify the effectiveness of the proposed method, author design a series of generation tasks and comparative experiments to validate the effectiveness of the proposed method. The generation tasks involve learning and generating seven common font styles using our model and comparative models. The experiments consisted of both qualitative and quantitative evaluation experiments. The specific results for generating 7 font styles can be foundin Figure 4 - Figure 10.In constructing our dataset, we drew inspiration from the well-established design process of the industry-leading company Fangzheng Font Library. We opted for a 500-character set as our training dataset, as depicted in Figure 2. These 500 characters are built upon the foundational 50 characters used to establish font styles. These structures encompass not only the 31 strokes of Chinese characters and the morphological variations of each stroke in different positions but also the fundamental structural forms of Chinese characters (e.g., left-right structure, top-bottom structure; enclosure structure, and semienclosure structure). Additionally, this dataset includes the majority of individual characters, compound characters, and most of the radical components. The selection of these 500 characters is pivotal in determining the overall consistency requirements for the entire font library.We utilized data from seven common font styles commonly employed in font design in the experiments. These styles include 1 Song style (Song), 1 Hei style (Black), 1 Kai style (Kai), 1 Fang Song style (FangSong), 1 Yuan style (Yuan), 1 Handwriting style (Handwriting), and 1 Decoration style (Decoration). Following the proposed 500-character dataset in this paper, datasets for these seven fonts were individually created. The fonts were downloaded in ttf or otf format from the respective font manufacturers' official websites. Python code was then used to preprocess the images into 256px*256px white-background black-font RGB images, forming seven sets of paired training datasets, each containing 500 characters. These datasets served as the source and target fonts in the model's comparative experiments to assess the model's style transfer capabilities. Additionally, 30 characters were randomly selected from the GB2312 character set, excluding the initial 500 characters, to form a test dataset for model inference and generation evaluation. The training was conducted on a cloud processor utilizing an NVIDIA RTX 3090 graphics processing unit (GPU), 60 GB of memory, and a 6-core Intel Xeon Gold 6142 processor (CPU). The experiments were run using PyCharm with remote connectivity to the cloud server. The experimental environment was configured with Python 3.10 and the torch 2.0 framework.The Song style font is downloaded from https://source.typekit.com/source-han-serif/cn/The Hei style font is downloaded from https://www.hanyi.com.cn/productdetail?id=831The Kai style font is downloaded from https://www.foundertype.com/index.php/FontInfo/index/id/241The Fang Song style font is downloaded from https://www.hanyi.com.cn/productdetail?id=10726The Yuan style font is downloaded from https://www.foundertype.com/index.php/FontInfo/index/id/219The Handwriting style font is downloaded from https://www.hanyi.com.cn/productdetail.php?id=9053&type=0The Decoration style font is downloaded from https://www.hanyi.com.cn/productdetail.php?id=608&type=0In common tasks involving character generation in seven typical Chinese character styles, the method and dataset employed in this paper demonstrate excellent generation results, demonstrating the superiority of the proposed method over the baseline method. For qualitative assessment, we visualized the results generated by the proposed model in Figures 2 to 9 and incorporated them into a questionnaire. As shown in Table 1, The final result indicate that the images generated by the proposed model surpass those generated by the other models for all 5 criteria. In particular, the model achieved the highest score in edge clarity evaluation, providing preliminary evidence that the proposed model optimizes character contour edges and enhances font generation quality. The models used in this study include ground truth font images for their respective tasks so the quantitative evaluation was conducted using image similarity metrics. Four image similarity evaluation metrics were employed to ensure the objectivity and reliability of the results: the structural similarity index (SSIM), feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE). A higher SSIM, FSIM, or PSNR indicates greater similarity between the generated and target images. Conversely, a smaller RMSE indicates lower dissimilarity and greater similarity between the generated and target images. As shown in Table 2, the proposed model obtained superior image similarity results compared to those of the comparative models in the 7 different style font generation tasks. On at least 6 out of the 7 experimental datasets, our proposed method achieved larger SSIM, FSIM, and PSNR values and smaller RMSE values, more objectively demonstrating the effectiveness of EdgeStyleGAN in optimizing character contour edges and font style transfer capabilities.For more details on the methodology, please read the related published article.The code is available from the corresponding author by request.<br>