  One of the factors limiting the performance of handwritten text recognition
(HTR) for stenography is the small amount of annotated training data. To
alleviate the problem of data scarcity, modern HTR methods often employ data
augmentation. However, due to specifics of the stenographic script, such
settings may not be directly applicable for stenography recognition. In this
work, we study 22 classical augmentation techniques, most of which are commonly
used for HTR of other scripts, such as Latin handwriting. Through extensive
experiments, we identify a group of augmentations, including for example
contained ranges of random rotation, shifts and scaling, that are beneficial to
the use case of stenography recognition. Furthermore, a number of augmentation
approaches, leading to a decrease in recognition performance, are identified.
Our results are supported by statistical hypothesis testing. Links to the
publicly available dataset and codebase are provided.
