  Scene text recognition models have advanced greatly in recent years. Inspired
by human reading we characterize two important scene text recognition models by
measuring their domains i.e. the range of stimulus images that they can read.
The domain specifies the ability of readers to generalize to different word
lengths, fonts, and amounts of occlusion. These metrics identify strengths and
weaknesses of existing models. Relative to the attention-based (Attn) model, we
discover that the connectionist temporal classification (CTC) model is more
robust to noise and occlusion, and better at generalizing to different word
lengths. Further, we show that in both models, adding noise to training images
yields better generalization to occlusion. These results demonstrate the value
of testing models till they break, complementing the traditional data science
focus on optimizing performance.
