  Despite recent advances in automatic text recognition, the performance
remains moderate when it comes to historical manuscripts. This is mainly
because of the scarcity of available labelled data to train the data-hungry
Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS)
provides a valid alternative to HTR due to the reduction in error rate, but it
is usually limited to a closed reference vocabulary. In this paper, we propose
a few-shot learning paradigm for spotting sequences of a few characters
(N-gram) that requires a small amount of labelled training data. We exhibit
that recognition of important n-grams could reduce the system's dependency on
vocabulary. In this case, an out-of-vocabulary (OOV) word in an input
handwritten line image could be a sequence of n-grams that belong to the
lexicon. An extensive experimental evaluation of our proposed
multi-representation approach was carried out on a subset of Bentham's
historical manuscript collections to obtain some really promising results in
this direction.
