  Low resource Handwritten Text Recognition (HTR) is a hard problem due to the
scarce annotated data and the very limited linguistic information (dictionaries
and language models). For example, in the case of historical ciphered
manuscripts, which are usually written with invented alphabets to hide the
message contents. Thus, in this paper we address this problem through a data
generation technique based on Bayesian Program Learning (BPL). Contrary to
traditional generation approaches, which require a huge amount of annotated
images, our method is able to generate human-like handwriting using only one
sample of each symbol in the alphabet. After generating symbols, we create
synthetic lines to train state-of-the-art HTR architectures in a segmentation
free fashion. Quantitative and qualitative analyses were carried out and
confirm the effectiveness of the proposed method.
