  Handwritten Word Recognition and Spotting is a challenging field dealing with
handwritten text possessing irregular and complex shapes. The design of deep
neural network models makes it necessary to extend training datasets in order
to introduce variations and increase the number of samples; word-retrieval is
therefore very difficult in low-resource scripts. Much of the existing
literature comprises preprocessing strategies which are seldom sufficient to
cover all possible variations. We propose the Adversarial Feature Deformation
Module (AFDM) that learns ways to elastically warp extracted features in a
scalable manner. The AFDM is inserted between intermediate layers and trained
alternatively with the original framework, boosting its capability to better
learn highly informative features rather than trivial ones. We test our
meta-framework, which is built on top of popular word-spotting and
word-recognition frameworks and enhanced by the AFDM, not only on extensive
Latin word datasets but also sparser Indic scripts. We record results for
varying training data sizes, and observe that our enhanced network generalizes
much better in the low-data regime; the overall word-error rates and mAP scores
are observed to improve as well.
