  In this work, our objective is to address the problems of generalization and
flexibility for text recognition in documents. We introduce a new model that
exploits the repetitive nature of characters in languages, and decouples the
visual representation learning and linguistic modelling stages. By doing this,
we turn text recognition into a shape matching problem, and thereby achieve
generalization in appearance and flexibility in classes. We evaluate the new
model on both synthetic and real datasets across different alphabets and show
that it can handle challenges that traditional architectures are not able to
solve without expensive retraining, including: (i) it can generalize to unseen
fonts without new exemplars from them; (ii) it can flexibly change the number
of classes, simply by changing the exemplars provided; and (iii) it can
generalize to new languages and new characters that it has not been trained for
by providing a new glyph set. We show significant improvements over
state-of-the-art models for all these cases.
