Given enough data of sufficient quality, HTR systems can achieve high accuracy, regardless of language, script or medium. Despite growing pooling of datasets, the question of the required quantity of training material still remains crucial for the transfer of models to out-of-domain documents, or the recognition of new scripts and under-resourced character classes. We propose a new data augmentation strategy, using generative adversarial networks (GAN). Inspired by synthetic lines generation for printed documents, our objective is to generate handwritten lines in order to massively produce data for a given style or under-resourced character class. Our approach, based on a variant of ScrabbleGAN, demonstrates the feasibility for various scripts, either in the presence of a high number and variety of abbreviations (Latin) and spellings or letter forms (Old French), in a situation of data scarcity (Armenian), or in the instance of a very cursive script (Arabic Maghribi). We then study the impact of synthetic line generation on HTR, by evaluating the gain for out-of-domain documents and under-resourced classes.