The aim of our research is to artificially generate fake historical manuscripts using generative adversarial networks, better known as GANs. At this stage, these experiments pursue two objectives: evaluating feasibility of generating realistic fake manuscripts under certain conditions of layout, script, or date, and creating artificial data for HTR training, as is done for printed materials with synthetic data. Ground truth creation is indeed a time consuming task, in particular for ancient languages for which we still lack specialists able to manually annotate documents. GAN appears as a relevant answer to this challenge, as they reach very convincing results in different scenarii. This short paper investigates the feasibility of the line-level approach for historical manuscripts.