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One of the factors limiting the performance of handwritten text recognition(HTR) for stenography is the small amount of annotated training data. Toalleviate the problem of data scarcity, modern HTR methods often employ dataaugmentation. However, due to specifics of the stenographic script, suchsettings may not be directly applicable for stenography recognition. In thiswork, we study 22 classical augmentation techniques, most of which are commonlyused for HTR of other scripts, such as Latin handwriting. Through extensiveexperiments, we identify a group of augmentations, including for examplecontained ranges of random rotation, shifts and scaling, that are beneficial tothe use case of stenography recognition. Furthermore, a number of augmentationapproaches, leading to a decrease in recognition performance, are identified.Our results are supported by statistical hypothesis testing. Links to thepublicly available dataset and codebase are provided.

A Study of Augmentation Methods for Handwritten Stenography Recognition