  The purpose of this paper is to enable the conversion between machine-printed
character images (i.e., font images) and handwritten character images through
machine learning. For this purpose, we propose a novel unpaired image-to-image
domain conversion method, CycleDM, which incorporates the concept of CycleGAN
into the diffusion model. Specifically, CycleDM has two internal conversion
models that bridge the denoising processes of two image domains. These
conversion models are efficiently trained without explicit correspondence
between the domains. By applying machine-printed and handwritten character
images to the two modalities, CycleDM realizes the conversion between them. Our
experiments for evaluating the converted images quantitatively and
qualitatively found that ours performs better than other comparable approaches.
