  Scene text recognition (STR) is a challenging task in computer vision due to
the large number of possible text appearances in natural scenes. Most STR
models rely on synthetic datasets for training since there are no sufficiently
big and publicly available labelled real datasets. Since STR models are
evaluated using real data, the mismatch between training and testing data
distributions results into poor performance of models especially on challenging
text that are affected by noise, artifacts, geometry, structure, etc. In this
paper, we introduce STRAug which is made of 36 image augmentation functions
designed for STR. Each function mimics certain text image properties that can
be found in natural scenes, caused by camera sensors, or induced by signal
processing operations but poorly represented in the training dataset. When
applied to strong baseline models using RandAugment, STRAug significantly
increases the overall absolute accuracy of STR models across regular and
irregular test datasets by as much as 2.10% on Rosetta, 1.48% on R2AM, 1.30% on
CRNN, 1.35% on RARE, 1.06% on TRBA and 0.89% on GCRNN. The diversity and
simplicity of API provided by STRAug functions enable easy replication and
validation of existing data augmentation methods for STR. STRAug is available
at https://github.com/roatienza/straug.
