Font design has become an essential part of multimedia. It has the ability to convey the mood and intention of the designer. However, creating a new font for Chinese characters takes a lot of e-ort because the language contains over 20 thousand character with complex morphological structures. Current font completion methods have many disadvantages. To address this problem, we propose StarFont, a font completion system that can automatically complete a whole font using a few-shot learning method. Our model takes several examples of a new font, learns the design style and applies it to the remaining characters to complete the font. Unlike existing models proposed for font generation, we treat each character not the font as a class and abandon reconstruction loss because the font’s ground truth is easier to obtain. Moreover, we combine multiple input images to generate new images, while existing methods use a one-toone approach. Compared to other deep learning-based font completion methods, our model requires fewer examples of the new font and generates better results. Both qualitative and quantitative methods show that our method is more advanced.