  This paper proposes a procedure to robustly train a scene text recognition
model using a learned surrogate of edit distance. The proposed method borrows
from self-paced learning and filters out the training examples that are hard
for the surrogate. The filtering is performed by judging the quality of the
approximation, using a ramp function, which is piece-wise differentiable,
enabling end-to-end training. Following the literature, the experiments are
conducted in a post-tuning setup, where a trained scene text recognition model
is tuned using the learned surrogate of edit distance. The efficacy is
demonstrated by improvements on various challenging scene text datasets such as
IIIT-5K, SVT, ICDAR, SVTP, and CUTE. The proposed method provides an average
improvement of $11.2 \%$ on total edit distance and an error reduction of
$9.5\%$ on accuracy.
