  This paper proposes a procedure to train a scene text recognition model using
a robust 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, 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.
