Images of historical Vietnamese steles allow historians to discover invaluable information regarding the past of the country, especially about the life of people in rural villages. Due to the sheer amount of available stone engravings and their diverseness, manual examination is difficult and time-consuming. Therefore, automatic document analysis methods based on machine learning could immensely facilitate this laborious work. However, creating ground truth for machine learning is also complex and time-consuming for human experts, which is why synthetic training samples greatly support learning while reducing human effort. In particular, they can be used to train deep neural networks for character detection and recognition. In this paper, we present a method for creating synthetic engravings and use it to create a new database composed of 26,901 synthetic Chu Nom characters in 21 different styles. Using a machine learning model for unpaired image-to-image translation, our approach is annotation-free, i.e. there is no need for human experts to label character images. A user study demonstrates that the synthetic engravings look realistic to the human eye.