  Scene text recognition (STR) enables computers to read text in natural scenes
such as object labels, road signs and instructions. STR helps machines perform
informed decisions such as what object to pick, which direction to go, and what
is the next step of action. In the body of work on STR, the focus has always
been on recognition accuracy. There is little emphasis placed on speed and
computational efficiency which are equally important especially for
energy-constrained mobile machines. In this paper we propose ViTSTR, an STR
with a simple single stage model architecture built on a compute and parameter
efficient vision transformer (ViT). On a comparable strong baseline method such
as TRBA with accuracy of 84.3%, our small ViTSTR achieves a competitive
accuracy of 82.6% (84.2% with data augmentation) at 2.4x speed up, using only
43.4% of the number of parameters and 42.2% FLOPS. The tiny version of ViTSTR
achieves 80.3% accuracy (82.1% with data augmentation), at 2.5x the speed,
requiring only 10.9% of the number of parameters and 11.9% FLOPS. With data
augmentation, our base ViTSTR outperforms TRBA at 85.2% accuracy (83.7% without
augmentation) at 2.3x the speed but requires 73.2% more parameters and 61.5%
more FLOPS. In terms of trade-offs, nearly all ViTSTR configurations are at or
near the frontiers to maximize accuracy, speed and computational efficiency all
at the same time.
